Computational intelligence paradigms: theory and applications using MATLAB
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| Sprache: | English |
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CRC Press
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| Beschreibung: | XXIII, 827 S. Ill., graph. Darst. |
| ISBN: | 9781439809020 |
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| 001 | BV035694016 | ||
| 003 | DE-604 | ||
| 005 | 20110505 | ||
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| 008 | 090826s2010 xxuad|| |||| 00||| eng d | ||
| 010 | |a 2009022113 | ||
| 020 | |a 9781439809020 |c hard back : alk. paper |9 978-1-4398-0902-0 | ||
| 024 | 8 | |a 9781439809020 | |
| 035 | |a (OCoLC)587764634 | ||
| 035 | |a (DE-599)BVBBV035694016 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
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| 044 | |a xxu |c US | ||
| 049 | |a DE-703 |a DE-11 | ||
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| 084 | |a ST 300 |0 (DE-625)143650: |2 rvk | ||
| 084 | |a ST 601 |0 (DE-625)143682: |2 rvk | ||
| 100 | 1 | |a Sumathi, Sai |d 1968- |e Verfasser |0 (DE-588)132409720 |4 aut | |
| 245 | 1 | 0 | |a Computational intelligence paradigms |b theory and applications using MATLAB |c S. Sumathi ; Surekha P. |
| 264 | 1 | |a Boca Raton, Fla. [u.a.] |b CRC Press |c 2010 | |
| 300 | |a XXIII, 827 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 630 | 0 | 4 | |a MATLAB |
| 650 | 4 | |a Computational intelligence | |
| 650 | 0 | 7 | |a MATLAB |0 (DE-588)4329066-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Soft Computing |0 (DE-588)4455833-8 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Soft Computing |0 (DE-588)4455833-8 |D s |
| 689 | 0 | 1 | |a MATLAB |0 (DE-588)4329066-8 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Paneerselvam, Surekha |d 1980- |e Sonstige |0 (DE-588)14055324X |4 oth | |
| 856 | 4 | 2 | |m Digitalisierung UB Bayreuth |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017748046&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-017748046 | ||
Datensatz im Suchindex
| _version_ | 1804139406734393344 |
|---|---|
| adam_text | Contents
Preface
xvii
1
Computational Intelligence
1
1.1
Introduction
........................ 1
1.2
Primary Classes of Problems for
CI
Techniques
.... 5
1.2.1
Optimization
................... 5
1.2.2
NP-Complete Problems
............. 6
1.3
Neural Networks
..................... 7
1.3.1
Feed Forward Neural Networks
......... 8
1.4
Fuzzy Systems
...................... 9
1.4.1
Fuzzy Sets
.................... 9
1.4.2
Fuzzy Controllers
................ 11
1.5
Evolutionary Computing
................. 12
1.5.1
Genetic Algorithms
............... 13
1.5.2
Genetic Programming
.............. 14
1.5.3
Evolutionary Programming
........... 15
1.5.4
Evolutionary Strategies
............. 15
1.6
Swarm Intelligence
.................... 16
1.7
Other Paradigms
..................... 17
1.7.1
Granular Computing
.............. 18
1.7.2
Chaos Theory
.................. 20
1.7.3
Artificial Immune Systems
........... 21
1.8
Hybrid Approaches
.................... 22
1.9
Relationship with Other Paradigms
........... 23
1.10
Challenges To Computational Intelligence
....... 25
Summary
............................. 26
Review Questions
........................ 27
2
Artificial Neural Networks with
MATLAB
29
2.1
Introduction
........................ 29
2.2
A Brief History of Neural Networks
........... 29
2.3
Artificial Neural Networks
................ 32
2.3.1
Comparison of Neural Network to the Brain
. 33
2.3.2
Artificial Neurons
................ 33
VI
2.3.3 Implementation
of Artificial Neuron Electroni¬
cally
........................ 35
2.3.4
Operations of Artificial Neural Network
.... 37
2.3.5
Training an Artificial Neural Network
..... 40
2.3.6
Comparison between Neural Networks. Tradi¬
tional Computing, and Expert Systems
.... 44
2.4
Neural Network Components
.............. 46
2.4.1
Teaching an Artificial Neural Network
..... 52
2.4.2
Learning Rates
.................. 54
2.4.3
Learning Laws
.................. 55
2.4.4
MATLAB
Implementation of Learning Rules
. 61
Summary
............................. 67
Review Questions
........................ 67
Artificial Neural Networks
-
Architectures and Algo¬
rithms
69
3.1
Introduction
........................ 69
3.2
Layered Architectures
.................. 71
3.2.1
Single-Layer Networks
.............. 71
3.2.2
Multilayer Networks
............... 73
3.3
Prediction Networks
................... 75
3.3.1
The Perceptron
................. 75
3.3.2
MATLAB
Implementation of a Perceptron Net¬
work
....................... 79
3.3.3
Feedforward Back-Propagation Network
.... 83
3.3.4
Implementation of BPN Using
MATLAB
... 91
3.3.5
Delta Bar Delta Network
............ 95
3.3.6
Extended Delta Bar Delta
........... 97
3.3.7
Directed Random Search Network
....... 100
3.3.8
Functional Link Artificial Neural Network
(FLANN) or Higher-Order Neural Network
. . 102
Summary
............................. 106
Review Questions
........................ 106
Classification and Association Neural Networks
109
4.1
Introduction
........................ 109
4.2
Neural Networks Based on Classification
........ 109
4.2.1
Learning Vector Quantization
......... 110
4.2.2
Implementation of LVQ in
MATLAB
..... 115
4.2.3
Counter-Propagation Network
......... 116
4.2.4
Probabilistic Neural Network
.......... 123
4.2.5
Implementation of the Probabilistic Neural Net
Using
MATLAB
................. 128
4.3
Data Association Networks
............... 129
4.3.1
Hopfield Network
................ 130
4.3.2
Implementation of Hopfield Network in MAT-
LAB
........................ 133
4.3.3
Boltzmann Machine
............... 134
4.3.4
Hamming Network
................ 137
4.3.5
Bi-Directional Associative Memory
....... 141
4.4
Data Conceptualization Networks
............ 146
4.4.1
Adaptive Resonance Network
.......... 146
4.4.2
Implementation of ART Algorithm in
MATLAB
151
4.4.3
Self-Organizing Map
............... 153
4.5
Applications Areas of ANN
............... 158
4.5.1
Language Processing
.............. 159
4.5.2
Character Recognition
.............. 160
4.5.3
Data Compression
................ 160
4.5.4
Pattern Recognition
............... 160
4.5.5
Signal Processing
................ 161
4.5.6
Financial
..................... 161
Summary
............................. 162
Review Questions
........................ 162
5
MATLAB
Programs to Implement Neural Networks
165
5.1
Illustration
1:
Coin Detection Using Euclidean Distance
(Hamming Net)
...................... 165
5.2
Illustration
2:
Learning Vector Quantization
-
Clustering
Data Drawn from Different Regions
.......... 171
5.3
Illustration
3:
Character Recognition Using Kohonen
Som
Network
....................... 174
5.4
Illustration
4:
The Hopfield Network as an Associative
Memory
.......................... 182
5.5
Illustration
5:
Generalized Delta Learning Rule and
Back-Propagation of Errors for a Multilayer Network
. 187
5.6
Illustration
6:
Classification of Heart Disease Using
Learning Vector Quantization
.............. 189
5.7
Illustration
7:
Neural Network Using
MATLAB Simulink
198
Summary
............................. 200
Review Questions
........................ 200
6
MATLAB-Based Fuzzy Systems
203
6.1
Introduction
........................ 203
6.2
Imprecision and Uncertainty
.............. 205
6.3
Crisp and Fuzzy Logic
.................. 205
6.4
Fuzzy Sets
......................... 207
Vlil
6.5
Universe
.......................... 209
6.6
Membership Functions
.................. 210
6.6.1
Types of Membership Functions
........ 212
6.6.2
Membership Functions in the
MATLAB
Fuzzy
Logic Toolbox
.................. 214
6.6.3
MATLAB
Code to Simulate Membership Func¬
tions
....................... 217
6.6.4
Translation of Parameters between Membership
Functions Using
MATLAB
........... 223
6.7
Singletons
......................... 224
6.8
Linguistic Variables
................... 225
6.9
Operations on Fuzzy Sets
................ 225
6.9.1
Fuzzy Complements
............... 227
6.9.2
Fuzzy Intersections: t-norms
.......... 230
6.9.3
Fuzzy Unions: t-conorms
............ 233
6.9.4
Combinations of Operations
.......... 235
6.9.5
MATLAB
Codes for Implementation of Fuzzy
Operations
.................... 236
6.9.6
Aggregation Operations
............. 239
6.10
Fuzzy Arithmetic
..................... 242
6.10.1
Arithmetic Operations on Intervals
...... 243
6.10.2
Arithmetic Operations on Fuzzy Numbers
. . 244
6.10.3
Fuzzy Arithmetic Using
MATLAB
Fuzzy Logic
Toolbox
...................... 246
6.11
Fuzzy Relations
...................... 247
6.12
Fuzzy Composition
.................... 251
6.12.1
MATLAB
Code to Implement Fuzzy Composi¬
tion
........................ 254
Summary
............................. 260
Review Questions
........................ 260
Fuzzy Inference and Expert Systems
261
7.1
Introduction
........................ 261
7.2
Fuzzy Rules
........................ 261
7.2.1
Generation of Fuzzy Rules
........... 262
7.2.2
Disintegration of Rules
............. 262
7.2.3
Aggregation of Rules
.............. 263
7.3
Fuzzy Expert System Model
............... 264
7.3.1
Fuzzification
................... 264
7.3.2
Fuzzy Rule Base and Fuzzy IF-THEN Rules
. 265
7.3.3
Fuzzy Inference Machine
............ 266
7.3.4
Defuzzification
.................. 267
їх
7.3.5 Implementation
of Denazification using MAT-
LAB Fuzzy Logic Toolbox
........... 273
7.4
Fuzzy Inference Methods
................ 276
7.4.1
Mamdani s Fuzzy Inference Method
...... 278
7.4.2
Takagi-Sugeno Fuzzy Inference Method
.... 281
7.4.3
Tsukamoto Fuzzy Inference Method
...... 282
7.5
Fuzzy Inference Systems in
MATLAB
......... 283
7.5.1
Mamdani-Type Fuzzy Inference
........ 286
7.5.2
Sugeno-Type Fuzzy Inference
......... 292
7.5.3
Conversion of Mamdani to Sugeno System
. . 295
7.6
Fuzzy Automata and Languages
............ 297
7.7
Fuzzy Control
....................... 298
7.7.1
Fuzzy Controllers
................ 300
7.7.2
A Fuzzy Controller in
MATLAB
........ 302
Summary
............................. 305
Review Questions
........................ 305
8
MATLAB
Illustrations on Fuzzy Systems
307
8.1
Illustration
1:
Application of Fuzzy Controller Using
MATLAB
—
Fuzzy Washing Machine
......... 307
8.2
Illustration
2 -
Fuzzy Control System for a Tanker Ship
317
8.3
Illustration
3 -
Approximation of Any Function Using
Fuzzy Logic
........................ 336
8.4
Illustration
4 -
Building Fuzzy Simulink Models
.... 343
Summary
............................. 348
Review Questions
........................ 348
9
Neuro-Fuzzy Modeling Using
MATLAB
351
9.1
Introduction
........................ 351
9.2
Cooperative and Concurrent Neuro-Fuzzy Systems
. . 352
9.3
Fused Neuro-Fuzzy Systems
............... 352
9.3.1
Fuzzy Adaptive Learning Control Network
(FALCON)
.................... 353
9.3.2
Adaptive Neuro-Fuzzy Inference System (AN-
FIS)
........................ 353
9.3.3
Generalized Approximate Reasoning-Based In¬
telligent Control (GARIC)
........... 355
9.3.4
Neuro-Fuzzy Control (NEFCON)
....... 356
9.3.5
Fuzzy Inference and Neural Network in Fuzzy
Inference Software (FINEST)
.......... 360
9.3.6
Fuzzy Net (FUN)
................ 362
9.3.7
Evolving Fuzzy Neural Network (EFuNN)
... 363
9.3.8 Self-Constructing
Neural
Fuzzy Inference Net¬
work (SONFIN)
................. 364
9.3.9
Evolutionary Design of Neuro-Fuzzy Systems
. 364
9.4
Hybrid Neuro-Fuzzy Model
—
ANFIS
......... 367
9.4.1
Architecture of Adaptive Neuro-Fuzzy Inference
System
...................... 367
9.4.2
Hybrid Learning Algorithm
.......... 370
9.5
Classification and Regression Trees
........... 372
9.5.1
CART
—
Introduction
............. 372
9.5.2
Node Splitting Criteria
............. 373
9.5.3
Classification Trees
............... 374
9.5.4
Regression Trees
................. 375
9.5.5
Computational Issues of CART
......... 375
9.5.6
Computational Steps
.............. 376
9.5.7
Accuracy Estimation in CART
......... 379
9.5.8
Advantages of Classification and Regression
Trees
....................... 380
9.6
Data Clustering Algorithms
............... 382
9.6.1
System Identification Using Fuzzy Clustering
. 382
9.6.2
Hard
С
-Means Clustering
............ 383
9.6.3
Fuzzy
С
-Means (FCM) Clustering
....... 386
9.6.4
Subtractive Clustering
.............. 388
9.6.5
Experiments
................... 389
Summary
............................. 393
Review Questions
........................ 393
10
Neuro-Fuzzy Modeling Using
MATLAB
395
10.1
Illustration
1 -
Fuzzy Art Map
............. 395
10.2
Illustration
2:
Fuzzy
С
-Means Clustering
—
Compara¬
tive Case Study
...................... 402
10.3
Illustration
3 -
Kmeans Clustering
........... 403
10.4
Illustration
4 -
Neuro-Fuzzy System Using Simulink
. 411
10.5
Illustration
5 -
Neuro-Fuzzy System Using Takagi-
Sugeno and ANFIS GUI of
MATLAB
......... 414
Summary
............................. 417
Review Questions
........................ 418
11
Evolutionary Computation Paradigms
419
11.1
Introduction
........................ 419
11.2
Evolutionary Computation
............... 420
11.3
Brief History of Evolutionary Computation
...... 422
11.4
Biological and Artificial Evolution
........... 423
11.4.1
Expressions Used in Evolutionary Computation
423
Xl
11.4.2
Biological Evolution Inspired by Nature
.... 423
11.4.3
Evolutionary Biology
.............. 425
11.5
Flow Diagram of a Typical Evolutionary Algorithm
. 428
11.6
Models of Evolutionary Computation
.......... 430
11.6.1
Genetic Algorithms (GA)
............ 430
11.6.2
Genetic Programming (GP)
........... 431
11.6.3
Evolutionary Programming (EP)
........ 435
11.6.4
Evolutionary Strategies (ESs)
......... 436
11.7
Evolutionary Algorithms
................. 439
11.7.1
Evolutionary Algorithms Parameters
..... 442
11.7.2
Solution Representation
............. 442
11.7.3
Fitness Function
................. 443
11.7.4
Initialization of Population Size
........ 444
11.7.5
Selection Mechanisms
.............. 444
11.7.6
Crossover Technique
............... 451
11.7.7
Mutation Operator
............... 455
11.7.8
Reproduction Operator
............. 458
11.8
Evolutionary Programming
............... 459
11.8.1
History
...................... 459
11.8.2
Procedure of Evolutionary Programming
. . . 460
11.8.3
EPs and GAs
................... 461
11.8.4
Algorithm of EP
................. 461
11.8.5
Flowchart
..................... 461
11.9
Evolutionary Strategies
................. 478
11.9.1
Solution Representation
............. 480
11.9.2
Mutation
..................... 480
11.9.3
Recombination
.................. 481
11.9.4
Population Assessment
............. 482
11.9.5
Convergence Criteria
.............. 483
11.9.6
Computational Considerations
......... 483
11.9.7
Algorithm Performance
............. 484
11.10
Advantages and Disadvantages of Evolutionary Compu¬
tation
........................... 485
Summary
............................. 487
Review Questions
........................ 488
12
Evolutionary Algorithms Implemented Using
MATLAB
491
12.1
Illustration
1:
Differential Evolution Optimizer
.... 491
12.2
Illustration
2:
Design of a Proportional-Derivative Con¬
troller Using Evolutionary Algorithm for Tanker Ship
Heading Regulation
................... 502
хп
12.3
Illustration
3:
Maximizing the Given One-Dimensional
Function with the Boundaries Using Evolutionary Algo¬
rithm
........................... 523
12.4
Illustration
4:
Multiobjective Optimization Using Evo¬
lutionary Algorithm (MOEA)
.............. 526
12.5
Illustration
5:
Evolutionary Strategy for Nonlinear Func¬
tion Minimization
.................... 541
Summary
............................. 545
Review Questions
........................ 545
13
MATLAB-Based Genetic Algorithm
547
13.1
Introduction
........................ 547
13.2
Encoding and Optimization Problems
......... 548
13.3
Historical Overview of Genetic Algorithm
....... 549
13.4
Genetic Algorithm Description
............. 550
13.5
Role of Genetic Algorithms
............... 552
13.6
Solution Representation of Genetic Algorithms
.... 553
13.7
Parameters of Genetic Algorithm
............ 554
13.7.1
Standard Parameter Settings
.......... 554
13.8
Schema Theorem and Theoretical Background
.... 555
13.8.1
Building Block Hypothesis
........... 556
13.8.2
The Dynamics of a Schema
........... 557
13.8.3
Illustrations Based on Schema Theorem
.... 559
13.9
Crossover Operators and Schemata
........... 563
13.9.1
1-Point Crossover
................ 563
13.9.2
2-Point Crossover
................ 563
13.9.3
Linkage and Defining Length
.......... 564
13.9.4
Linkage and Inversion
.............. 565
13.10
Genotype and Fitness
.................. 566
13.11
Advanced Operators in GA
............... 567
13.11.1
Inversion and Reordering
............ 567
13.11.2
Epistasis
..................... 567
13.11.3
Deception
..................... 568
13.11.4
Mutation and
Naïve
Evolution
......... 569
13.11.5
Niche and Speciation
.............. 569
13.11.6
Restricted Mating
................ 569
13.11.7
Diploidy and Dominance
............ 569
13.12
G A
Versus Traditional Search and Optimization Meth¬
ods
............................. 570
13.12.1
Neural Nets
................... 570
13.12.2
Random Search
................. 571
13.12.3
Gradient Methods
................ 571
13.12.4
Iterated Search
.................. 571
ХНІ
13.12.5
Simulated Annealing
.............. 572
13.13
Benefits of GA
...................... 572
13.14
MATLAB
Programs on Genetic Algorithm
...... 573
13.14.1
Illustration
1:
Maximizing the Given One-
Dimensional Function within Given Boundaries
573
13.14.2
Illustration
2:
Solving Economic Dispatch Prob¬
lem Using Genetic Algorithm
.......... 576
13.14.3
Illustration
3:
Traveling Salesman Problem
. . 580
Summary
............................. 587
Review Questions
........................ 588
14
Genetic Programming
591
14.1
Introduction
........................ 591
14.2
Growth of Genetic Programming
............ 594
14.3
The Lisp Programming Language
............ 595
14.4
Functionality of Genetic Programming
......... 596
14.4.1
Generation of an Individual and Population
. . 596
14.4.2
Creating a Random Population
......... 597
14.4.3
Fitness Test
................... 599
14.4.4
Functions and Terminals
............ 599
14.4.5
The Genetic Operations
............. 599
14.4.6
Selection Functions
............... 600
14.4.7
MATLAB
Routine for Selection
........ 601
14.4.8
Crossover Operation
............... 604
14.4.9
MATLAB
Routine for Crossover
........ 607
14.4.10
Mutation Operation
............... 608
14.4.11
MATLAB
Routine for Mutation
........ 608
14.5
Genetic Programming in Machine Learning
...... 610
14.6
Elementary Steps of Genetic Programming
...... 612
14.6.1
The Terminal Set
................ 613
14.6.2
The Function Set
................ 613
14.6.3
The Fitness Function
.............. 613
14.6.4
The Algorithm Control Parameters
...... 614
14.6.5
The Termination Criterion
........... 614
14.7
Flowchart of Genetic Programming
........... 615
14.8
Benefits of Genetic Programming
............ 618
14.9
MATLAB
Examples Using Genetic Programming
. . . 618
14.9.1
Illustration
1:
Static Function Identification
. . 618
14.9.2
Illustration
2:
Dynamical Input-Output Model
Identification
................... 629
14.9.3
Illustration
3 -
Symbolic Regression Problem
Using Genetic Programming Toolbox
..... 632
Summary
............................. 645
XIV
Review
Questions
........................ 646
15 MATLAB-Based
Swarm Intelligence
649
15.1
Introduction
to Swarms .................
649
15.2
Biological Background
.................. 650
15.3
Swarm Robots
...................... 652
15.4
Stability of Swarms
.................... 653
15.5
Swarm Intelligence
.................... 654
15.5.1
Properties of a Swarm Intelligence System
. . 655
15.6
Particle Swarm Optimization (PSO)
.......... 656
15.6.1
Mathematical Model of PSO
.......... 657
15.6.2
Algorithm of PSO
................ 658
15.6.3
Parameters and Tuning of Parameters in PSO
658
15.6.4
Neighborhood Topologies
............ 659
15.7
Extended Models of PSO
................ 661
15.7.1
PSO-I
....................... 661
15.7.2
PSO-II
...................... 661
15.7.3
PSO-III
...................... 662
15.7.4
PSO-IV
...................... 663
15.8
Ant Colony Optimization
................ 663
15.8.1
Mathematical Model of
АСО
.......... 664
15.8.2
Ant Colony Optimization Algorithm
...... 664
15.9
Studies and Applications of Swarm Intelligence
.... 665
15.9.1
Ant-based Routing
................ 665
15.9.2
Clustering Behavior of Ants
.......... 665
15.9.3
Graph Coloring
................. 666
15.9.4
Machine Scheduling
............... 666
15.9.5
Quadratic Knapsack Problem
.......... 667
15.9.6
Traveling Salesman Problem
.......... 668
15.10
MATLAB
Examples of Swarm Intelligence
....... 668
15.10.1
Illustration
1:
Simulation of the Movement of
Swarm to Minimize the Objective Function
. 669
15.10.2
Illustration
2:
Behavior of Particle Swarm Op¬
timization
.................... 671
15.10.3
Illustration
3:
Ant Colony Optimization to De¬
termine the Shortest Path
............ 684
15.10.4
Illustration
4:
Ant Algorithm for the Quadratic
Assignment Problem (QAP)
.......... 687
Summary
............................. 691
Review Questions
........................ 691
A Glossary of Terms
693
В
List of Abbreviations
737
С
MATLAB
Toolboxes Based on
CI
741
C.I Genetic Algorithm Toolbox for
MATLAB
....... 741
С.
2
Fuzzy Logic Toolbox
2.2.7................ 744
C.3 Neural Network Toolbox
6.0............... 746
C.4 Genetic Algorithm and Direct Search Toolbox
2.2 . . 752
C.5 GPLAB
-
A Genetic Programming Toolbox for MAT-
LAB
............................ 754
D
Emerging Software Packages
763
D.I BUGS
........................... 763
D.2 ComputerAnts
...................... 763
D.3 DGenesis
......................... 764
D.4 Ease
............................ 764
D.5 Evolution Machine
.................... 764
D.6 Evolutionary Objects
................... 765
D.7 GAC, GAL
........................ 765
D.8 GAGA
........................... 766
D.9 GAGS
........................... 766
D.10 GAlib
........................... 767
D.ll
GALOPPS ........................ 767
D.12 GAMusic
......................... 768
D.13 GANNET
......................... 769
D.14 GA Workbench
...................... 769
D.15
GECO
........................... 769
D.
16
Genesis
.......................... 770
D.17 GENEsYs
......................... 770
D.18 GenET
.......................... 771
D.19 Genie
........................... 771
D.20
Genitor
.......................... 771
D.21 GENlib
.......................... 772
D.22 GENOCOP
........................ 772
D.23 GPEIST
.......................... 772
D.24 Imogene
.......................... 773
D.25 JAG
............................ 773
D.26 LibGA
........................... 774
D.27 mGA
........................... 774
D.28 PGA
............................ 774
D.29 PGAPack
......................... 775
D.30 SGA-C, SGA-Cube
.................... 776
D.31 Splicer
........................... 776
D.32 Trans-Dimensional Learning
............... 777
XVI
D.33
WOLF
........................... 777
D.34 XGenetic
......................... 778
D.
35
XFUZZY: A Simulation Environment for Fuzzy Logic
Control Systems
..................... 778
D.36 ART*Enterprise
..................... 779
D.37 COMDALE/C, COMDALE/X, and ProcessVision
. . 779
E
Research Projects
781
E.I An Evolutionary Algorithm for Global Optimization
Based on Level-Set Evolution and Latin Squares
. . . 781
E.2 Evolving Problems to Learn about Particle Swarm Op¬
timizers and Other Search Algorithms
......... 782
E.3 Analog Genetic Encoding for the Evolution of Circuits
and Networks
....................... 782
E.4 A Runtime Analysis of Evolutionary Algorithms for Con¬
strained Optimization Problems
............. 783
E.5 Solving the Register Allocation Problem for Embedded
Systems Using a Hybrid Evolutionary Algorithm
. . . 783
E.6 Semantic Understanding of General Linguistic Items by
Means of Fuzzy Set Theory
............... 784
E.7 Fuzzy Evaluation of Heart Rate Signals for Mental Stress
Assessment
........................ 785
E.8 An Ant Colony Optimization Approach to the Proba¬
bilistic Traveling Salesman Problem
.......... 785
E.9 Neural Processing of Symbolic Data
.......... 786
References
787
Index
821
|
| any_adam_object | 1 |
| author | Sumathi, Sai 1968- |
| author_GND | (DE-588)132409720 (DE-588)14055324X |
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| callnumber-first | Q - Science |
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| callnumber-raw | Q342 |
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| classification_rvk | ST 300 ST 601 |
| ctrlnum | (OCoLC)587764634 (DE-599)BVBBV035694016 |
| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV035694016 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T21:43:35Z |
| institution | BVB |
| isbn | 9781439809020 |
| language | English |
| lccn | 2009022113 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017748046 |
| oclc_num | 587764634 |
| open_access_boolean | |
| owner | DE-703 DE-11 |
| owner_facet | DE-703 DE-11 |
| physical | XXIII, 827 S. Ill., graph. Darst. |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | CRC Press |
| record_format | marc |
| spelling | Sumathi, Sai 1968- Verfasser (DE-588)132409720 aut Computational intelligence paradigms theory and applications using MATLAB S. Sumathi ; Surekha P. Boca Raton, Fla. [u.a.] CRC Press 2010 XXIII, 827 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier MATLAB Computational intelligence MATLAB (DE-588)4329066-8 gnd rswk-swf Soft Computing (DE-588)4455833-8 gnd rswk-swf Soft Computing (DE-588)4455833-8 s MATLAB (DE-588)4329066-8 s DE-604 Paneerselvam, Surekha 1980- Sonstige (DE-588)14055324X oth Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017748046&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Sumathi, Sai 1968- Computational intelligence paradigms theory and applications using MATLAB MATLAB Computational intelligence MATLAB (DE-588)4329066-8 gnd Soft Computing (DE-588)4455833-8 gnd |
| subject_GND | (DE-588)4329066-8 (DE-588)4455833-8 |
| title | Computational intelligence paradigms theory and applications using MATLAB |
| title_auth | Computational intelligence paradigms theory and applications using MATLAB |
| title_exact_search | Computational intelligence paradigms theory and applications using MATLAB |
| title_full | Computational intelligence paradigms theory and applications using MATLAB S. Sumathi ; Surekha P. |
| title_fullStr | Computational intelligence paradigms theory and applications using MATLAB S. Sumathi ; Surekha P. |
| title_full_unstemmed | Computational intelligence paradigms theory and applications using MATLAB S. Sumathi ; Surekha P. |
| title_short | Computational intelligence paradigms |
| title_sort | computational intelligence paradigms theory and applications using matlab |
| title_sub | theory and applications using MATLAB |
| topic | MATLAB Computational intelligence MATLAB (DE-588)4329066-8 gnd Soft Computing (DE-588)4455833-8 gnd |
| topic_facet | MATLAB Computational intelligence Soft Computing |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017748046&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT sumathisai computationalintelligenceparadigmstheoryandapplicationsusingmatlab AT paneerselvamsurekha computationalintelligenceparadigmstheoryandapplicationsusingmatlab |