Principles of adaptive filters and self-learning systems:
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| Main Author: | |
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| Format: | Book |
| Language: | English |
| Published: |
London
Springer
2005
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| Series: | Advanced textbooks in control and signal processing
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturverz. S. [373] - 380 |
| Physical Description: | XXII, 386 S. graph. Darst. |
| ISBN: | 1852339845 9781852339845 |
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| 100 | 1 | |a Zaknich, Anthony |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Principles of adaptive filters and self-learning systems |c A. Zaknich |
| 264 | 1 | |a London |b Springer |c 2005 | |
| 300 | |a XXII, 386 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Advanced textbooks in control and signal processing | |
| 500 | |a Literaturverz. S. [373] - 380 | ||
| 650 | 4 | |a Filtres adaptatifs - Conception - Mathématiques | |
| 650 | 4 | |a Intelligence artificielle | |
| 650 | 4 | |a Künstliche Intelligenz | |
| 650 | 4 | |a Mathematik | |
| 650 | 4 | |a Adaptive filters |x Design and construction |x Mathematics | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 0 | 7 | |a Adaptives Filter |0 (DE-588)4141377-5 |2 gnd |9 rswk-swf |
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2005 A. ZAKNICH PRINCIPLES OF ADAPTIVE FILTERS AND SELF-LEARNING SYSTEMS
WITH 95 FIGURES 123 ANTHONY ZAKNICH, PHD SCHOOL OF ENGINEERING SCIENCE,
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ACID-FREE PAPER SPIN 10978566 CONTENTS PART I INTRODUCTION 1 1
ADAPTIVE FILTERING
..............................................................................
...............3 1.1 LINEAR ADAPTIVE FILTERS
.............................................................................5
1.1.1 LINEAR ADAPTIVE FILTER ALGORITHMS
..............................................7 1.2 NONLINEAR ADAPTIVE
FILTERS........................................................................9
1.2.1 ADAPTIVE VOLTERRA
FILTERS.............................................................9
1.3 NONCLASSICAL ADAPTIVE SYSTEMS
..............................................................10
1.3.1 ARTIFICIAL NEURAL NETWORKS
........................................................10 1.3.2
FUZZY
LOGIC...............................................................................11
1.3.3 GENETIC ALGORITHMS
...................................................................11
1.4 A BRIEF HISTORY AND OVERVIEW OF CLASSICAL
THEORIES..............................12 1.4.1 LINEAR ESTIMATION
THEORY.............................................. ............12
1.4.2 LINEAR ADAPTIVE
FILTERS..............................................................13
1.4.3 ADAPTIVE SIGNAL PROCESSING
APPLICATIONS..................................14 1.4.4 ADAPTIVE
CONTROL.......................................................................16
1.5 A BRIEF HISTORY AND OVERVIEW OF NONCLASSICAL
THEORIES........................17 1.5.1 ARTIFICIAL NEURAL NETWORKS
........................................................17 1.5.2
FUZZY
LOGIC...............................................................................18
1.5.3 GENETIC ALGORITHMS
...................................................................18
1.6 FUNDAMENTALS OF ADAPTIVE
NETWORKS......................................................19 1.7
CHOICE OF ADAPTIVE FILTER ALGORITHM
......................................................23 2 LINEAR
SYSTEMS AND STOCHASTIC PROCESSES
......................................................25 2.1 BASIC
CONCEPTS OF LINEAR
SYSTEMS..........................................................27
2.2 DISCRETE-TIME SIGNALS AND SYSTEMS
.........................................................29 2.3 THE
DISCRETE FOURIER TRANSFORM (DFT)
..................................................31 2.3.1 DISCRETE
LINEAR CONVOLUTION USING THE DFT..............................32
2.3.2 DIGITAL SAMPLING THEORY
...........................................................33
2.3.2.1 ANALOGUE INTERPRETATION FORMULA...............................37
2.4 THE FAST FOURIER
TRANSFORM....................................................................37
2.5 THE Z-TRANSFORM
.....................................................................................40
2.5.1 RELATIONSHIP BETWEEN LAPLACE TRANSFORM AND
Z-TRANSFORM......40 2.5.1.1 BILATERAL Z-TRANSFORM
................................................41 CONTENTS XVI
2.5.1.2 UNILATERAL Z-TRANSFORM
..............................................42 2.5.1.3 REGION
OF CONVERGENCE (ROC) FOR THE Z-TRANSFORM .42 2.5.1.4 REGION OF
CONVERGENCE (ROC) FOR GENERAL SIGNALS..43 2.5.2 GENERAL PROPERTIES
OF THE DFT AND Z-TRANSFORM.......................44 2.6 SUMMARY OF
DISCRETE-TIME LSI
SYSTEMS................................................46 2.7 SPECIAL
CLASSES OF FILTERS
........................................................................48
2.7.1 PHASE RESPONSE FROM FREQUENCY MAGNITUDE
RESPONSE.............50 2.8 LINEAR ALGEBRA
SUMMARY........................................................................51
2.8.1 VECTORS
......................................................................................51
2.8.2 LINEAR INDEPENDENCE, VECTOR SPACES, AND BASIC
VECTORS..........52 2.8.3
MATRICES......................................................................
...............53 2.8.4 LINEAR EQUATIONS
.......................................................................55
2.8.5 SPECIAL MATRICES
........................................................................56
2.8.6 QUADRATIC AND HERMITIAN FORMS
................................................59 2.8.7
EIGENVALUES AND
EIGENVECTORS...................................................59 2.9
INTRODUCTION TO STOCHASTIC
PROCESSES.......................................................61
2.10 RANDOM SIGNALS
......................................................................................63
2.11 BASIC DESCRIPTIVE MODELS OF RANDOM
SIGNALS........................................64 2.11.1 THE MEAN
SQUARE VALUE AND VARIANCE......................................64
2.11.2 THE PROBABILITY DENSITY
FUNCTION..............................................65 2.11.3
JOINTLY DISTRIBUTED RANDOM
VARIABLES.......................................68 2.11.4 THE
EXPECTATION OPERATOR
.........................................................68 2.11.5
THE AUTOCORRELATION AND RELATED
FUNCTIONS..............................69 2.11.6 POWER SPECTRAL
DENSITY FUNCTIONS.............................................72
2.11.7 COHERENCE
FUNCTION...................................................................73
2.11.8 DISCRETE ERGODIC RANDOM SIGNAL STATISTICS
...............................74 2.11.9 AUTOCOVARIANCE AND
AUTOCORRELATION MATRICES..........................75 2.11.10
SPECTRUM OF A RANDOM
PROCESS.................................................76 2.11.11
FILTERING OF RANDOM
PROCESSES..................................................78
2.11.12 IMPORTANT EXAMPLES OF RANDOM PROCESSES
...............................80 2.11.12.1 GAUSSIAN PROCESS
.......................................................80
2.11.12.2 WHITE
NOISE...............................................................80
2.11.12.3 WHITE SEQUENCES
.......................................................81
2.11.12.4 GAUSS-MARKOV
PROCESSES...........................................81 2.11.12.5
THE RANDOM TELEGRAPH WAVE...................................81 2.12
EXERCISES.....................................................................
............................82 2.12.1
PROBLEMS......................................................................
..............82 PART II MODELLING 87 3 OPTIMISATION AND LEAST SQUARE
ESTIMATION ..................................................89 3.1
OPTIMISATION
THEORY...............................................................................89
3.2 OPTIMISATION METHODS IN DIGITAL FILTER
DESIGN.......................................91 3.3 LEAST SQUARES
ESTIMATION........................................................................95
3.4 LEAST SQUARES MAXIMUM LIKELIHOOD ESTIMATOR
......................................97 CONTENTS XVII 3.5
LINEAR REGRESSION * FITTING DATA TO A LINE
.............................................98 3.6 GENERAL LINEAR
LEAST SQUARES
.................................................................99
3.7 A SHIP POSITIONING EXAMPLE OF
LSE.....................................................100 3.8
ACOUSTIC POSITIONING SYSTEM
EXAMPLE..................................................104 3.9
MEASURE OF LSE PRECISION
....................................................................108
3.10 MEASURE OF LSE
RELIABILITY...................................................................109
3.11 LIMITATIONS OF
LSE................................................................................110
3.12 ADVANTAGES OF LSE
...............................................................................110
3.13 THE SINGULAR VALUE
DECOMPOSITION......................................................111
3.13.1 THE
PSEUDOINVERSE...................................................................112
3.13.2 COMPUTATION OF THE
SVD.........................................................112
3.13.2.1 THE JACOBI ALGORITHM
..............................................112 3.13.2.2 THE
QR ALGORITHM...................................................115
3.14
EXERCISES.....................................................................
..........................116 3.14.1
PROBLEMS......................................................................
............116 4 PARAMETRIC SIGNAL AND SYSTEM MODELLING
..................................................119 4.1 THE
ESTIMATION PROBLEM
.......................................................................120
4.2 DETERMINISTIC SIGNAL AND SYSTEM
MODELLING.........................................121 4.2.1 THE
LEAST SQUARES METHOD
......................................................122 4.2.2 THE
PADE APPROXIMATION METHOD ...........................................124
4.2.3 PRONY*S METHOD
.......................................................................127
4.2.3.1 ALL-POLE MODELLING USING PRONY*S METHOD..............130
4.2.3.2 LINEAR PREDICTION
.....................................................131 4.2.3.3
DIGITAL WIENER FILTER................................................132
4.2.4 AUTOCORRELATION AND COVARIANCE
METHODS...............................133 4.3 STOCHASTIC SIGNAL
MODELLING
................................................................137
4.3.1 AUTOREGRESSIVE MOVING AVERAGE
MODELS................................137 4.3.2 AUTOREGRESSIVE
MODELS............................................................139
4.3.3 MOVING AVERAGE
MODELS.........................................................140 4.4
THE LEVINSON-DURBIN RECURSION AND LATTICE
FILTERS.............................141 4.4.1 THE LEVINSON-DURBIN
RECURSION DEVELOPMENT .......................142 4.4.1.1 EXAMPLE
OF THE LEVINSON-DURBIN RECURSION............145 4.4.2 THE LATTICE
FILTER.....................................................................146
4.4.3 THE CHOLESKY DECOMPOSITION
.................................................149 4.4.4 THE
LEVINSON
RECURSION..........................................................151
4.5
EXERCISES.....................................................................
..........................154 4.5.1
PROBLEMS......................................................................
............154 PART III CLASSICAL FILTERS AND SPECTRAL ANALYSIS 157 5
OPTIMUM WIENER FILTER
................................................................................159
5.1 DERIVATION OF THE IDEAL CONTINUOUS-TIME WIENER FILTER
........................160 5.2 THE IDEAL DISCRETE-TIME FIR WIENER
FILTER ...........................................162 5.2.1 GENERAL
NOISE FIR WIENER FILTERING .......................................164
CONTENTS XVIII 5.2.2 FIR WIENER LINEAR PREDICTION
.................................................165 5.3 DISCRETE-TIME
CAUSAL IIR WIENER FILTER
................................................167 5.3.1 CAUSAL
IIR WIENER FILTERING
....................................................169 5.3.2
WIENER DECONVOLUTION
............................................................170 5.4
EXERCISES.....................................................................
..........................171 5.4.1
PROBLEMS......................................................................
............171 6 OPTIMAL KALMAN FILTER
..............................................................................
...173 6.1 BACKGROUND TO THE KALMAN FILTER
........................................................173 6.2 THE
KALMAN
FILTER.................................................................................174
6.2.1 KALMAN FILTER EXAMPLES
..........................................................181 6.3
KALMAN FILTER FOR SHIP
MOTION..............................................................185
6.3.1 KALMAN TRACKING FILTER
PROPER................................................186 6.3.2
SIMPLE EXAMPLE OF A DYNAMIC SHIP MODELS...........................189
6.3.3 STOCHASTIC MODELS
...................................................................192
6.3.4 ALTERNATE SOLUTION
MODELS.......................................................192
6.3.5 ADVANTAGES OF KALMAN
FILTERING..............................................193 6.3.6
DISADVANTAGES OF KALMAN FILTERING
.........................................193 6.4 EXTENDED KALMAN
FILTER
........................................................................194
6.5
EXERCISES.....................................................................
..........................194 6.5.1
PROBLEMS......................................................................
............194 7 POWER SPECTRAL DENSITY ANALYSIS
.................................................................197
7.1 POWER SPECTRAL DENSITY ESTIMATION TECHNIQUES
...................................198 7.2 NONPARAMETRIC SPECTRAL
DENSITY ESTIMATION .........................................199
7.2.1 PERIODOGRAM POWER SPECTRAL DENSITY
ESTIMATION....................199 7.2.2 MODIFIED PERIODOGRAM * DATA
WINDOWING .............................203 7.2.3 BARTLETT*S METHOD
* PERIODOGRAM AVERAGING ..........................205 7.2.4 WELCH*S
METHOD
......................................................................206
7.2.5 BLACKMAN-TUKEY
METHOD........................................................208
7.2.6 PERFORMANCE COMPARISONS OF NONPARAMETRIC MODELS.............209
7.2.7 MINIMUM VARIANCE METHOD
....................................................209 7.2.8
MAXIMUM ENTROPY (ALL POLES) METHOD...................................212
7.3 PARAMETRIC SPECTRAL DENSITY
ESTIMATION................................................215 7.3.1
AUTOREGRESSIVE
METHODS..........................................................215
7.3.1.1 YULE-WALKER
APPROACH............................................216 7.3.1.2
COVARIANCE, LEAST SQUARES AND BURG METHODS ........217 7.3.1.3
MODEL ORDER SELECTION FOR THE AUTOREGRESSIVE METHODS
..........................................218 7.3.2 MOVING AVERAGE
METHOD ........................................................218
7.3.3 AUTOREGRESSIVE MOVING AVERAGE
METHOD................................219 7.3.4 HARMONIC
METHODS..................................................................219
7.3.4.1 EIGENDECOMPOSITION OF THE AUTOCORRELATION
MATRIX
......................................................................219
7.3.4.1.1 PISARENKO*S METHOD
.................................221 7.3.4.1.2
MUSIC.....................................................222
CONTENTS XIX 7.4
EXERCISES.....................................................................
..........................223 7.4.1
PROBLEMS......................................................................
............223 PART IV ADAPTIVE FILTER THEORY 225 8 ADAPTIVE FINITE
IMPULSE RESPONSE FILTERS
...................................................227 8.1 ADAPTIVE
INTERFERENCE CANCELLING
.........................................................228 8.2 LEAST
MEAN SQUARES ADAPTATION
...........................................................230
8.2.1 OPTIMUM WIENER
SOLUTION.......................................................231
8.2.2 THE METHOD OF STEEPEST GRADIENT DESCENT
SOLUTION................233 8.2.3 THE LMS ALGORITHM
SOLUTION..................................................235 8.2.4
STABILITY OF THE LMS
ALGORITHM...............................................237 8.2.5
THE NORMALISED LMS
ALGORITHM.............................................239 8.3
RECURSIVE LEAST SQUARES ESTIMATION
.....................................................239 8.3.1 THE
EXPONENTIALLY WEIGHTED RECURSIVE LEAST SQUARES
ALGORITHM...................................................................240
8.3.2 RECURSIVE LEAST SQUARES ALGORITHM
CONVERGENCE...................243 8.3.2.1 CONVERGENCE OF THE
FILTER COEFFICIENTS IN THE
MEAN..................................................................243
8.3.2.2 CONVERGENCE OF THE FILTER COEFFICIENTS IN THE
MEAN SQUARE......................................................244
8.3.2.3 CONVERGENCE OF THE RLS ALGORITHM IN THE MEAN
SQUARE......................................................244
8.3.3 THE RLS ALGORITHM AS A KALMAN
FILTER...................................244 8.4
EXERCISES.....................................................................
..........................245 8.4.1
PROBLEMS......................................................................
............245 9 FREQUENCY DOMAIN ADAPTIVE FILTERS
...........................................................247 9.1
FREQUENCY DOMAIN
PROCESSING..............................................................247
9.1.1 TIME DOMAIN BLOCK ADAPTIVE FILTERING
..................................248 9.1.2 FREQUENCY DOMAIN
ADAPTIVE FILTERING ....................................249
9.1.2.1 THE OVERLAP-SAVE
METHOD.......................................251 9.1.2.2 THE
OVERLAP-ADD METHOD .......................................254
9.1.2.3 THE CIRCULAR CONVOLUTION METHOD...........................255
9.1.2.4 COMPUTATIONAL
COMPLEXITY......................................256 9.2
EXERCISES.....................................................................
..........................256 9.2.1
PROBLEMS......................................................................
............256 10 ADAPTIVE VOLTERRA FILTERS
.............................................................................2
57 10.1 NONLINEAR FILTERS
...................................................................................257
10.2 THE VOLTERRA SERIES EXPANSION
.............................................................259 10.3
A LMS ADAPTIVE SECOND-ORDER VOLTERRA FILTER
....................................259 10.4 A LMS ADAPTIVE QUADRATIC
FILTER ........................................................261
10.5 A RLS ADAPTIVE QUADRATIC FILTER
.........................................................262 10.6
EXERCISES.....................................................................
..........................264 CONTENTS XX 10.6.1
PROBLEMS......................................................................
............264 11 ADAPTIVE CONTROL SYSTEMS
............................................................................267
11.1 MAIN THEORETICAL
ISSUES........................................................................268
11.2 INTRODUCTION TO MODEL-REFERENCE ADAPTIVE
SYSTEMS..............................270 11.2.1 THE GRADIENT
APPROACH...........................................................271
11.2.2 LEAST SQUARES ESTIMATION
........................................................273 11.2.3
A GENERAL SINGLE-INPUT-SINGLE-OUTPUT MRAS..........................274
11.2.4 LYAPUNOV*S STABILITY THEORY
...................................................277 11.3
INTRODUCTION TO SELF-TUNING
REGULATORS..................................................280
11.3.1 INDIRECT SELF-TUNING
REGULATORS................................................282
11.3.2 DIRECT SELF-TUNING
REGULATORS..................................................283 11.4
RELATIONS BETWEEN MRAS AND
STR......................................................284 11.5
APPLICATIONS..................................................................
........................285 PART V NONCLASSICAL ADAPTIVE SYSTEMS 287
12 INTRODUCTION TO NEURAL NETWORKS
................................................................289
12.1 ARTIFICIAL NEURAL
NETWORKS....................................................................289
12.1.1
DEFINITIONS...................................................................
............290 12.1.2 THREE MAIN TYPES
...................................................................290
12.1.3 SPECIFIC ARTIFICIAL NEURAL NETWORK
PARADIGMS........................292 12.1.4 ARTIFICIAL NEURAL
NETWORKS AS BLACK BOXED ............................293 12.1.5
IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORKS........................294
12.1.6 WHEN TO USE AN ARTIFICIAL NEURAL NETWORK
.............................295 12.1.7 HOW TO USE AN ARTIFICIAL
NEURAL NETWORK ...............................295 12.1.8 ARTIFICIAL
NEURAL NETWORK GENERAL APPLICATIONS.....................296 12.1.9
SIMPLE APPLICATION EXAMPLES
.................................................297 12.1.9.1
SHEEP EATING PHASE IDENTIFICATION FROM JAW SOUNDS
.....................................................................298
12.1.9.2 HYDRATE PARTICLE ISOLATION IN SEM
IMAGES...............298 12.1.9.3 OXALATE NEEDLE DETECTION IN
MICROSCOPE IMAGES....299 12.1.9.4 WATER LEVEL DETERMINATION FROM
RESONANT SOUND ANALYSIS
........................................................299
12.1.9.5 NONLINEAR SIGNAL FILTERING
.......................................299 12.1.9.6 A MOTOR
CONTROL EXAMPLE.......................................300 12.2 A
THREE-LAYER MULTI-LAYER PERCEPTRON MODEL
.......................................300 12.2.1 MLP
BACKPROPAGATION-OF-ERROR LEARNING................................302
12.2.2 DERIVATION OF BACKPROPAGATION-OF-ERROR LEARNING
..................303 12.2.2.1 CHANGE IN ERROR DUE TO OUTPUT
LAYER WEIGHTS ........303 12.2.2.2 CHANGE IN ERROR DUE TO HIDDEN
LAYER WEIGHTS........304 12.2.2.3 THE WEIGHT ADJUSTMENTS
.........................................305 12.2.2.4 ADDITIONAL
MOMENTUM FACTOR .................................307 12.2.3 NOTES
ON CLASSIFICATION AND FUNCTION MAPPING.......................308
12.2.4 MLP APPLICATION AND TRAINING
ISSUES.....................................308 CONTENTS XXI
12.3
EXERCISES.....................................................................
..........................310 12.3.1
PROBLEMS......................................................................
............310 13 INTRODUCTION TO FUZZY LOGIC SYSTEMS
..........................................................313 13.1
BASIC FUZZY LOGIC
................................................................................313
13.1.1 FUZZY LOGIC MEMBERSHIP FUNCTIONS
.......................................314 13.1.2 FUZZY LOGIC
OPERATIONS ..........................................................315
13.1.3 FUZZY LOGIC
RULES...................................................................316
13.1.4 FUZZY LOGIC DEFUZZIFICATION
...................................................317 13.2 FUZZY
LOGIC CONTROL DESIGN
.................................................................318
13.2.1 FUZZY LOGIC
CONTROLLERS..........................................................319
13.2.1.1 CONTROL RULE
CONSTRUCTION.......................................319 13.2.1.2
PARAMETER TUNING ....................................................321
13.2.1.3 CONTROL RULE REVISION
.............................................322 13.3 FUZZY ARTIFICIAL
NEURAL
NETWORKS.........................................................322
13.4 FUZZY APPLICATIONS
...............................................................................323
14 INTRODUCTION TO GENETIC ALGORITHMS
............................................................325 14.1
A GENERAL GENETIC ALGORITHM
...............................................................326
14.2 THE COMMON HYPOTHESIS
REPRESENTATION.............................................327 14.3
GENETIC ALGORITHM
OPERATORS................................................................329
14.4 FITNESS FUNCTIONS
..................................................................................330
14.5 HYPOTHESIS
SEARCHING............................................................................330
14.6 GENETIC PROGRAMMING
...........................................................................331
14.7 APPLICATIONS OF GENETIC
PROGRAMMING..................................................332
14.7.1 FILTER CIRCUIT DESIGN APPLICATIONS OF GAS AND GP
.................333 14.7.2 TIC-TAC-TO GAME PLAYING APPLICATION OF
GAS .........................334 PART VI ADAPTIVE FILTER APPLICATION 337
15 APPLICATIONS OF ADAPTIVE SIGNAL PROCESSING
...............................................339 15.1 ADAPTIVE
PREDICTION
..............................................................................340
15.2 ADAPTIVE MODELLING
..............................................................................342
15.3 ADAPTIVE TELEPHONE ECHO
CANCELLING...................................................343 15.4
ADAPTIVE EQUALISATION OF COMMUNICATION
CHANNELS.............................344 15.5 ADAPTIVE SELF-TUNING
FILTERS..................................................................346
15.6 ADAPTIVE NOISE CANCELLING
...................................................................346
15.7 FOCUSED TIME DELAY ESTIMATION FOR RANGING
.......................................348 15.7.1 ADAPTIVE ARRAY
PROCESSING......................................................349
15.8 OTHER ADAPTIVE FILTER
APPLICATIONS.......................................................350
15.8.1 ADAPTIVE 3-D SOUND
SYSTEMS..................................................350 15.8.2
MICROPHONE
ARRAYS..................................................................351
15.8.3 NETWORK AND ACOUSTIC ECHO
CANCELLATION...............................352 15.8.4 REAL-WORLD
ADAPTIVE FILTERING APPLICATIONS............................353 CONTENTS
XXII 16 GENERIC ADAPTIVE FILTER STRUCTURES
.............................................................355
16.1 SUB-BAND ADAPTIVE FILTERS
....................................................................355
16.2 SUB-SPACE ADAPTIVE FILTERS
...................................................................358
16.2.1 MPNN
MODEL..........................................................................360
16.2.2 APPROXIMATELY PIECEWISE LINEAR REGRESSION
MODEL...............362 16.2.3 THE SUB-SPACE ADAPTIVE FILTER
MODEL.....................................364 16.2.4 EXAMPLE
APPLICATIONS OF THE SSAF MODEL..............................366
16.2.4.1 LOUDSPEAKER 3-D FREQUENCY RESPONSE MODEL ........367
16.2.4.2 VELOCITY OF SOUND IN WATER 3-D MODEL...................369
16.3 DISCUSSION AND OVERVIEW OF THE SSAF
.................................................370 REFERENCES
..............................................................................
.............................373 INDEX
..............................................................................
......................................381
|
| adam_txt |
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ACID-FREE PAPER SPIN 10978566 CONTENTS PART I INTRODUCTION 1 1
ADAPTIVE FILTERING
.
.3 1.1 LINEAR ADAPTIVE FILTERS
.5
1.1.1 LINEAR ADAPTIVE FILTER ALGORITHMS
.7 1.2 NONLINEAR ADAPTIVE
FILTERS.9
1.2.1 ADAPTIVE VOLTERRA
FILTERS.9
1.3 NONCLASSICAL ADAPTIVE SYSTEMS
.10
1.3.1 ARTIFICIAL NEURAL NETWORKS
.10 1.3.2
FUZZY
LOGIC.11
1.3.3 GENETIC ALGORITHMS
.11
1.4 A BRIEF HISTORY AND OVERVIEW OF CLASSICAL
THEORIES.12 1.4.1 LINEAR ESTIMATION
THEORY. .12
1.4.2 LINEAR ADAPTIVE
FILTERS.13
1.4.3 ADAPTIVE SIGNAL PROCESSING
APPLICATIONS.14 1.4.4 ADAPTIVE
CONTROL.16
1.5 A BRIEF HISTORY AND OVERVIEW OF NONCLASSICAL
THEORIES.17 1.5.1 ARTIFICIAL NEURAL NETWORKS
.17 1.5.2
FUZZY
LOGIC.18
1.5.3 GENETIC ALGORITHMS
.18
1.6 FUNDAMENTALS OF ADAPTIVE
NETWORKS.19 1.7
CHOICE OF ADAPTIVE FILTER ALGORITHM
.23 2 LINEAR
SYSTEMS AND STOCHASTIC PROCESSES
.25 2.1 BASIC
CONCEPTS OF LINEAR
SYSTEMS.27
2.2 DISCRETE-TIME SIGNALS AND SYSTEMS
.29 2.3 THE
DISCRETE FOURIER TRANSFORM (DFT)
.31 2.3.1 DISCRETE
LINEAR CONVOLUTION USING THE DFT.32
2.3.2 DIGITAL SAMPLING THEORY
.33
2.3.2.1 ANALOGUE INTERPRETATION FORMULA.37
2.4 THE FAST FOURIER
TRANSFORM.37
2.5 THE Z-TRANSFORM
.40
2.5.1 RELATIONSHIP BETWEEN LAPLACE TRANSFORM AND
Z-TRANSFORM.40 2.5.1.1 BILATERAL Z-TRANSFORM
.41 CONTENTS XVI
2.5.1.2 UNILATERAL Z-TRANSFORM
.42 2.5.1.3 REGION
OF CONVERGENCE (ROC) FOR THE Z-TRANSFORM .42 2.5.1.4 REGION OF
CONVERGENCE (ROC) FOR GENERAL SIGNALS.43 2.5.2 GENERAL PROPERTIES
OF THE DFT AND Z-TRANSFORM.44 2.6 SUMMARY OF
DISCRETE-TIME LSI
SYSTEMS.46 2.7 SPECIAL
CLASSES OF FILTERS
.48
2.7.1 PHASE RESPONSE FROM FREQUENCY MAGNITUDE
RESPONSE.50 2.8 LINEAR ALGEBRA
SUMMARY.51
2.8.1 VECTORS
.51
2.8.2 LINEAR INDEPENDENCE, VECTOR SPACES, AND BASIC
VECTORS.52 2.8.3
MATRICES.
.53 2.8.4 LINEAR EQUATIONS
.55
2.8.5 SPECIAL MATRICES
.56
2.8.6 QUADRATIC AND HERMITIAN FORMS
.59 2.8.7
EIGENVALUES AND
EIGENVECTORS.59 2.9
INTRODUCTION TO STOCHASTIC
PROCESSES.61
2.10 RANDOM SIGNALS
.63
2.11 BASIC DESCRIPTIVE MODELS OF RANDOM
SIGNALS.64 2.11.1 THE MEAN
SQUARE VALUE AND VARIANCE.64
2.11.2 THE PROBABILITY DENSITY
FUNCTION.65 2.11.3
JOINTLY DISTRIBUTED RANDOM
VARIABLES.68 2.11.4 THE
EXPECTATION OPERATOR
.68 2.11.5
THE AUTOCORRELATION AND RELATED
FUNCTIONS.69 2.11.6 POWER SPECTRAL
DENSITY FUNCTIONS.72
2.11.7 COHERENCE
FUNCTION.73
2.11.8 DISCRETE ERGODIC RANDOM SIGNAL STATISTICS
.74 2.11.9 AUTOCOVARIANCE AND
AUTOCORRELATION MATRICES.75 2.11.10
SPECTRUM OF A RANDOM
PROCESS.76 2.11.11
FILTERING OF RANDOM
PROCESSES.78
2.11.12 IMPORTANT EXAMPLES OF RANDOM PROCESSES
.80 2.11.12.1 GAUSSIAN PROCESS
.80
2.11.12.2 WHITE
NOISE.80
2.11.12.3 WHITE SEQUENCES
.81
2.11.12.4 GAUSS-MARKOV
PROCESSES.81 2.11.12.5
THE RANDOM TELEGRAPH WAVE.81 2.12
EXERCISES.
.82 2.12.1
PROBLEMS.
.82 PART II MODELLING 87 3 OPTIMISATION AND LEAST SQUARE
ESTIMATION .89 3.1
OPTIMISATION
THEORY.89
3.2 OPTIMISATION METHODS IN DIGITAL FILTER
DESIGN.91 3.3 LEAST SQUARES
ESTIMATION.95
3.4 LEAST SQUARES MAXIMUM LIKELIHOOD ESTIMATOR
.97 CONTENTS XVII 3.5
LINEAR REGRESSION * FITTING DATA TO A LINE
.98 3.6 GENERAL LINEAR
LEAST SQUARES
.99
3.7 A SHIP POSITIONING EXAMPLE OF
LSE.100 3.8
ACOUSTIC POSITIONING SYSTEM
EXAMPLE.104 3.9
MEASURE OF LSE PRECISION
.108
3.10 MEASURE OF LSE
RELIABILITY.109
3.11 LIMITATIONS OF
LSE.110
3.12 ADVANTAGES OF LSE
.110
3.13 THE SINGULAR VALUE
DECOMPOSITION.111
3.13.1 THE
PSEUDOINVERSE.112
3.13.2 COMPUTATION OF THE
SVD.112
3.13.2.1 THE JACOBI ALGORITHM
.112 3.13.2.2 THE
QR ALGORITHM.115
3.14
EXERCISES.
.116 3.14.1
PROBLEMS.
.116 4 PARAMETRIC SIGNAL AND SYSTEM MODELLING
.119 4.1 THE
ESTIMATION PROBLEM
.120
4.2 DETERMINISTIC SIGNAL AND SYSTEM
MODELLING.121 4.2.1 THE
LEAST SQUARES METHOD
.122 4.2.2 THE
PADE APPROXIMATION METHOD .124
4.2.3 PRONY*S METHOD
.127
4.2.3.1 ALL-POLE MODELLING USING PRONY*S METHOD.130
4.2.3.2 LINEAR PREDICTION
.131 4.2.3.3
DIGITAL WIENER FILTER.132
4.2.4 AUTOCORRELATION AND COVARIANCE
METHODS.133 4.3 STOCHASTIC SIGNAL
MODELLING
.137
4.3.1 AUTOREGRESSIVE MOVING AVERAGE
MODELS.137 4.3.2 AUTOREGRESSIVE
MODELS.139
4.3.3 MOVING AVERAGE
MODELS.140 4.4
THE LEVINSON-DURBIN RECURSION AND LATTICE
FILTERS.141 4.4.1 THE LEVINSON-DURBIN
RECURSION DEVELOPMENT .142 4.4.1.1 EXAMPLE
OF THE LEVINSON-DURBIN RECURSION.145 4.4.2 THE LATTICE
FILTER.146
4.4.3 THE CHOLESKY DECOMPOSITION
.149 4.4.4 THE
LEVINSON
RECURSION.151
4.5
EXERCISES.
.154 4.5.1
PROBLEMS.
.154 PART III CLASSICAL FILTERS AND SPECTRAL ANALYSIS 157 5
OPTIMUM WIENER FILTER
.159
5.1 DERIVATION OF THE IDEAL CONTINUOUS-TIME WIENER FILTER
.160 5.2 THE IDEAL DISCRETE-TIME FIR WIENER
FILTER .162 5.2.1 GENERAL
NOISE FIR WIENER FILTERING .164
CONTENTS XVIII 5.2.2 FIR WIENER LINEAR PREDICTION
.165 5.3 DISCRETE-TIME
CAUSAL IIR WIENER FILTER
.167 5.3.1 CAUSAL
IIR WIENER FILTERING
.169 5.3.2
WIENER DECONVOLUTION
.170 5.4
EXERCISES.
.171 5.4.1
PROBLEMS.
.171 6 OPTIMAL KALMAN FILTER
.
.173 6.1 BACKGROUND TO THE KALMAN FILTER
.173 6.2 THE
KALMAN
FILTER.174
6.2.1 KALMAN FILTER EXAMPLES
.181 6.3
KALMAN FILTER FOR SHIP
MOTION.185
6.3.1 KALMAN TRACKING FILTER
PROPER.186 6.3.2
SIMPLE EXAMPLE OF A DYNAMIC SHIP MODELS.189
6.3.3 STOCHASTIC MODELS
.192
6.3.4 ALTERNATE SOLUTION
MODELS.192
6.3.5 ADVANTAGES OF KALMAN
FILTERING.193 6.3.6
DISADVANTAGES OF KALMAN FILTERING
.193 6.4 EXTENDED KALMAN
FILTER
.194
6.5
EXERCISES.
.194 6.5.1
PROBLEMS.
.194 7 POWER SPECTRAL DENSITY ANALYSIS
.197
7.1 POWER SPECTRAL DENSITY ESTIMATION TECHNIQUES
.198 7.2 NONPARAMETRIC SPECTRAL
DENSITY ESTIMATION .199
7.2.1 PERIODOGRAM POWER SPECTRAL DENSITY
ESTIMATION.199 7.2.2 MODIFIED PERIODOGRAM * DATA
WINDOWING .203 7.2.3 BARTLETT*S METHOD
* PERIODOGRAM AVERAGING .205 7.2.4 WELCH*S
METHOD
.206
7.2.5 BLACKMAN-TUKEY
METHOD.208
7.2.6 PERFORMANCE COMPARISONS OF NONPARAMETRIC MODELS.209
7.2.7 MINIMUM VARIANCE METHOD
.209 7.2.8
MAXIMUM ENTROPY (ALL POLES) METHOD.212
7.3 PARAMETRIC SPECTRAL DENSITY
ESTIMATION.215 7.3.1
AUTOREGRESSIVE
METHODS.215
7.3.1.1 YULE-WALKER
APPROACH.216 7.3.1.2
COVARIANCE, LEAST SQUARES AND BURG METHODS .217 7.3.1.3
MODEL ORDER SELECTION FOR THE AUTOREGRESSIVE METHODS
.218 7.3.2 MOVING AVERAGE
METHOD .218
7.3.3 AUTOREGRESSIVE MOVING AVERAGE
METHOD.219 7.3.4 HARMONIC
METHODS.219
7.3.4.1 EIGENDECOMPOSITION OF THE AUTOCORRELATION
MATRIX
.219
7.3.4.1.1 PISARENKO*S METHOD
.221 7.3.4.1.2
MUSIC.222
CONTENTS XIX 7.4
EXERCISES.
.223 7.4.1
PROBLEMS.
.223 PART IV ADAPTIVE FILTER THEORY 225 8 ADAPTIVE FINITE
IMPULSE RESPONSE FILTERS
.227 8.1 ADAPTIVE
INTERFERENCE CANCELLING
.228 8.2 LEAST
MEAN SQUARES ADAPTATION
.230
8.2.1 OPTIMUM WIENER
SOLUTION.231
8.2.2 THE METHOD OF STEEPEST GRADIENT DESCENT
SOLUTION.233 8.2.3 THE LMS ALGORITHM
SOLUTION.235 8.2.4
STABILITY OF THE LMS
ALGORITHM.237 8.2.5
THE NORMALISED LMS
ALGORITHM.239 8.3
RECURSIVE LEAST SQUARES ESTIMATION
.239 8.3.1 THE
EXPONENTIALLY WEIGHTED RECURSIVE LEAST SQUARES
ALGORITHM.240
8.3.2 RECURSIVE LEAST SQUARES ALGORITHM
CONVERGENCE.243 8.3.2.1 CONVERGENCE OF THE
FILTER COEFFICIENTS IN THE
MEAN.243
8.3.2.2 CONVERGENCE OF THE FILTER COEFFICIENTS IN THE
MEAN SQUARE.244
8.3.2.3 CONVERGENCE OF THE RLS ALGORITHM IN THE MEAN
SQUARE.244
8.3.3 THE RLS ALGORITHM AS A KALMAN
FILTER.244 8.4
EXERCISES.
.245 8.4.1
PROBLEMS.
.245 9 FREQUENCY DOMAIN ADAPTIVE FILTERS
.247 9.1
FREQUENCY DOMAIN
PROCESSING.247
9.1.1 TIME DOMAIN BLOCK ADAPTIVE FILTERING
.248 9.1.2 FREQUENCY DOMAIN
ADAPTIVE FILTERING .249
9.1.2.1 THE OVERLAP-SAVE
METHOD.251 9.1.2.2 THE
OVERLAP-ADD METHOD .254
9.1.2.3 THE CIRCULAR CONVOLUTION METHOD.255
9.1.2.4 COMPUTATIONAL
COMPLEXITY.256 9.2
EXERCISES.
.256 9.2.1
PROBLEMS.
.256 10 ADAPTIVE VOLTERRA FILTERS
.2
57 10.1 NONLINEAR FILTERS
.257
10.2 THE VOLTERRA SERIES EXPANSION
.259 10.3
A LMS ADAPTIVE SECOND-ORDER VOLTERRA FILTER
.259 10.4 A LMS ADAPTIVE QUADRATIC
FILTER .261
10.5 A RLS ADAPTIVE QUADRATIC FILTER
.262 10.6
EXERCISES.
.264 CONTENTS XX 10.6.1
PROBLEMS.
.264 11 ADAPTIVE CONTROL SYSTEMS
.267
11.1 MAIN THEORETICAL
ISSUES.268
11.2 INTRODUCTION TO MODEL-REFERENCE ADAPTIVE
SYSTEMS.270 11.2.1 THE GRADIENT
APPROACH.271
11.2.2 LEAST SQUARES ESTIMATION
.273 11.2.3
A GENERAL SINGLE-INPUT-SINGLE-OUTPUT MRAS.274
11.2.4 LYAPUNOV*S STABILITY THEORY
.277 11.3
INTRODUCTION TO SELF-TUNING
REGULATORS.280
11.3.1 INDIRECT SELF-TUNING
REGULATORS.282
11.3.2 DIRECT SELF-TUNING
REGULATORS.283 11.4
RELATIONS BETWEEN MRAS AND
STR.284 11.5
APPLICATIONS.
.285 PART V NONCLASSICAL ADAPTIVE SYSTEMS 287
12 INTRODUCTION TO NEURAL NETWORKS
.289
12.1 ARTIFICIAL NEURAL
NETWORKS.289
12.1.1
DEFINITIONS.
.290 12.1.2 THREE MAIN TYPES
.290
12.1.3 SPECIFIC ARTIFICIAL NEURAL NETWORK
PARADIGMS.292 12.1.4 ARTIFICIAL NEURAL
NETWORKS AS BLACK BOXED .293 12.1.5
IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORKS.294
12.1.6 WHEN TO USE AN ARTIFICIAL NEURAL NETWORK
.295 12.1.7 HOW TO USE AN ARTIFICIAL
NEURAL NETWORK .295 12.1.8 ARTIFICIAL
NEURAL NETWORK GENERAL APPLICATIONS.296 12.1.9
SIMPLE APPLICATION EXAMPLES
.297 12.1.9.1
SHEEP EATING PHASE IDENTIFICATION FROM JAW SOUNDS
.298
12.1.9.2 HYDRATE PARTICLE ISOLATION IN SEM
IMAGES.298 12.1.9.3 OXALATE NEEDLE DETECTION IN
MICROSCOPE IMAGES.299 12.1.9.4 WATER LEVEL DETERMINATION FROM
RESONANT SOUND ANALYSIS
.299
12.1.9.5 NONLINEAR SIGNAL FILTERING
.299 12.1.9.6 A MOTOR
CONTROL EXAMPLE.300 12.2 A
THREE-LAYER MULTI-LAYER PERCEPTRON MODEL
.300 12.2.1 MLP
BACKPROPAGATION-OF-ERROR LEARNING.302
12.2.2 DERIVATION OF BACKPROPAGATION-OF-ERROR LEARNING
.303 12.2.2.1 CHANGE IN ERROR DUE TO OUTPUT
LAYER WEIGHTS .303 12.2.2.2 CHANGE IN ERROR DUE TO HIDDEN
LAYER WEIGHTS.304 12.2.2.3 THE WEIGHT ADJUSTMENTS
.305 12.2.2.4 ADDITIONAL
MOMENTUM FACTOR .307 12.2.3 NOTES
ON CLASSIFICATION AND FUNCTION MAPPING.308
12.2.4 MLP APPLICATION AND TRAINING
ISSUES.308 CONTENTS XXI
12.3
EXERCISES.
.310 12.3.1
PROBLEMS.
.310 13 INTRODUCTION TO FUZZY LOGIC SYSTEMS
.313 13.1
BASIC FUZZY LOGIC
.313
13.1.1 FUZZY LOGIC MEMBERSHIP FUNCTIONS
.314 13.1.2 FUZZY LOGIC
OPERATIONS .315
13.1.3 FUZZY LOGIC
RULES.316
13.1.4 FUZZY LOGIC DEFUZZIFICATION
.317 13.2 FUZZY
LOGIC CONTROL DESIGN
.318
13.2.1 FUZZY LOGIC
CONTROLLERS.319
13.2.1.1 CONTROL RULE
CONSTRUCTION.319 13.2.1.2
PARAMETER TUNING .321
13.2.1.3 CONTROL RULE REVISION
.322 13.3 FUZZY ARTIFICIAL
NEURAL
NETWORKS.322
13.4 FUZZY APPLICATIONS
.323
14 INTRODUCTION TO GENETIC ALGORITHMS
.325 14.1
A GENERAL GENETIC ALGORITHM
.326
14.2 THE COMMON HYPOTHESIS
REPRESENTATION.327 14.3
GENETIC ALGORITHM
OPERATORS.329
14.4 FITNESS FUNCTIONS
.330
14.5 HYPOTHESIS
SEARCHING.330
14.6 GENETIC PROGRAMMING
.331
14.7 APPLICATIONS OF GENETIC
PROGRAMMING.332
14.7.1 FILTER CIRCUIT DESIGN APPLICATIONS OF GAS AND GP
.333 14.7.2 TIC-TAC-TO GAME PLAYING APPLICATION OF
GAS .334 PART VI ADAPTIVE FILTER APPLICATION 337
15 APPLICATIONS OF ADAPTIVE SIGNAL PROCESSING
.339 15.1 ADAPTIVE
PREDICTION
.340
15.2 ADAPTIVE MODELLING
.342
15.3 ADAPTIVE TELEPHONE ECHO
CANCELLING.343 15.4
ADAPTIVE EQUALISATION OF COMMUNICATION
CHANNELS.344 15.5 ADAPTIVE SELF-TUNING
FILTERS.346
15.6 ADAPTIVE NOISE CANCELLING
.346
15.7 FOCUSED TIME DELAY ESTIMATION FOR RANGING
.348 15.7.1 ADAPTIVE ARRAY
PROCESSING.349
15.8 OTHER ADAPTIVE FILTER
APPLICATIONS.350
15.8.1 ADAPTIVE 3-D SOUND
SYSTEMS.350 15.8.2
MICROPHONE
ARRAYS.351
15.8.3 NETWORK AND ACOUSTIC ECHO
CANCELLATION.352 15.8.4 REAL-WORLD
ADAPTIVE FILTERING APPLICATIONS.353 CONTENTS
XXII 16 GENERIC ADAPTIVE FILTER STRUCTURES
.355
16.1 SUB-BAND ADAPTIVE FILTERS
.355
16.2 SUB-SPACE ADAPTIVE FILTERS
.358
16.2.1 MPNN
MODEL.360
16.2.2 APPROXIMATELY PIECEWISE LINEAR REGRESSION
MODEL.362 16.2.3 THE SUB-SPACE ADAPTIVE FILTER
MODEL.364 16.2.4 EXAMPLE
APPLICATIONS OF THE SSAF MODEL.366
16.2.4.1 LOUDSPEAKER 3-D FREQUENCY RESPONSE MODEL .367
16.2.4.2 VELOCITY OF SOUND IN WATER 3-D MODEL.369
16.3 DISCUSSION AND OVERVIEW OF THE SSAF
.370 REFERENCES
.
.373 INDEX
.
.381 |
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| any_adam_object_boolean | 1 |
| author | Zaknich, Anthony |
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| discipline | Elektrotechnik / Elektronik / Nachrichtentechnik |
| discipline_str_mv | Elektrotechnik / Elektronik / Nachrichtentechnik |
| format | Book |
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| illustrated | Illustrated |
| index_date | 2024-07-02T16:10:31Z |
| indexdate | 2024-07-09T20:48:52Z |
| institution | BVB |
| isbn | 1852339845 9781852339845 |
| language | English |
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| physical | XXII, 386 S. graph. Darst. |
| publishDate | 2005 |
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| publisher | Springer |
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| series2 | Advanced textbooks in control and signal processing |
| spelling | Zaknich, Anthony Verfasser aut Principles of adaptive filters and self-learning systems A. Zaknich London Springer 2005 XXII, 386 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Advanced textbooks in control and signal processing Literaturverz. S. [373] - 380 Filtres adaptatifs - Conception - Mathématiques Intelligence artificielle Künstliche Intelligenz Mathematik Adaptive filters Design and construction Mathematics Artificial intelligence Adaptives Filter (DE-588)4141377-5 gnd rswk-swf Lernendes System (DE-588)4120666-6 gnd rswk-swf Lernendes System (DE-588)4120666-6 s DE-604 Adaptives Filter (DE-588)4141377-5 s SWB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015204681&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Zaknich, Anthony Principles of adaptive filters and self-learning systems Filtres adaptatifs - Conception - Mathématiques Intelligence artificielle Künstliche Intelligenz Mathematik Adaptive filters Design and construction Mathematics Artificial intelligence Adaptives Filter (DE-588)4141377-5 gnd Lernendes System (DE-588)4120666-6 gnd |
| subject_GND | (DE-588)4141377-5 (DE-588)4120666-6 |
| title | Principles of adaptive filters and self-learning systems |
| title_auth | Principles of adaptive filters and self-learning systems |
| title_exact_search | Principles of adaptive filters and self-learning systems |
| title_exact_search_txtP | Principles of adaptive filters and self-learning systems |
| title_full | Principles of adaptive filters and self-learning systems A. Zaknich |
| title_fullStr | Principles of adaptive filters and self-learning systems A. Zaknich |
| title_full_unstemmed | Principles of adaptive filters and self-learning systems A. Zaknich |
| title_short | Principles of adaptive filters and self-learning systems |
| title_sort | principles of adaptive filters and self learning systems |
| topic | Filtres adaptatifs - Conception - Mathématiques Intelligence artificielle Künstliche Intelligenz Mathematik Adaptive filters Design and construction Mathematics Artificial intelligence Adaptives Filter (DE-588)4141377-5 gnd Lernendes System (DE-588)4120666-6 gnd |
| topic_facet | Filtres adaptatifs - Conception - Mathématiques Intelligence artificielle Künstliche Intelligenz Mathematik Adaptive filters Design and construction Mathematics Artificial intelligence Adaptives Filter Lernendes System |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015204681&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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