Adaptive modelling, estimation and fusion from data: a neurofuzzy approach
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2002
|
| Schriftenreihe: | Advanced information processing
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVI, 323 S. graph. Darst. |
| ISBN: | 3540426868 |
Internformat
MARC
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| 100 | 1 | |a Harris, Christopher J. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Adaptive modelling, estimation and fusion from data |b a neurofuzzy approach |c Chris Harris ; Xia Hong ; Qiang Gan |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2002 | |
| 300 | |a XVI, 323 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
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| 689 | 1 | 0 | |a Neuro-Fuzzy-System |0 (DE-588)4560677-8 |D s |
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| 700 | 1 | |a Hong, Xia |e Verfasser |4 aut | |
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Datensatz im Suchindex
| _version_ | 1804138982556041216 |
|---|---|
| adam_text | Contents
1. An
introduction
to modelling and learning algorithms
.... 1
1.1
Introduction to modelling
............................... 1
1.2
Modelling, control and learning algorithms
................. 7
1.3
The learning problem
................................... 9
1.4
Book philosophy and contents overview
................... 13
1.4.1
Book overview
................................... 13
1.4.2
A historical perspective of adaptive modelling
and control
...................................... 21
2.
Basic concepts of data-based modelling
................... 25
2.1
Introduction
........................................... 25
2.2
State-space models versus input-output models
............ 26
2.2.1
Conversion of state-space models
to input-output models
........................... 26
2.2.2
Conversion of input-output models
to state-space models
............................. 28
2.3
Nonlinear modelling by basis function expansion
............ 29
2.4
Model parameter estimation
............................. 31
2.5
Model quality
.......................................... 33
2.5.1
The bias-variance dilemma
........................ 33
2.5.2
Bias-variance balance by model structure
régularisation
34
2.6
Reproducing kernels and
régularisation
networks
........... 39
2.7
Model selection methods
................................ 42
2.7.1
Model selection criteria
........................... 43
2.7.2
Model selection criteria sensitivity
.................. 44
2.7.3
Correlation tests
................................. 45
2.8
An example: time series modelling
........................ 49
3.
Learning laws for linear-in-the-parameters networks
...... 53
3.1
Introduction to learning
................................. 53
3.2
Error or performance surfaces
............................ 55
3.3
Batch learning laws
..................................... 58
3.3.1
General learning laws
............................. 58
3.3.2
Gradient descent algorithms
....................... 59
XIV Contents
3.4
Instantaneous learning laws
.............................. 61
3.4.1
Least mean squares learning
....................... 61
3.4.2
Normalised least mean squares learning
............. 62
3.4.3
NLMS weight convergence
......................... 63
3.4.4
Recursive least squares estimation
.................. 67
3.5
Gradient noise and normalised condition numbers
.......... 68
3.6
Adaptive learning rates
................................. 70
4.
Fuzzy and neurofuzzy modelling
.......................... 71
4.1
Introduction to fuzzy and neurofuzzy systems
.............. 71
4.2
Fuzzy systems
......................................... 74
4.2.1
Fuzzy sets
....................................... 75
4.2.2
Fuzzy operators
.................................. 83
4.2.3
Fuzzy relation surfaces
............................ 87
4.2.4
Inferencing
...................................... 88
4.2.5
Fuzzification and denazification
.................... 89
4.3
Functional mapping and neurofuzzy models
................ 91
4.4
Takagi-Sugeno local neurofuzzy model
.................... 95
4.5
Neurofuzzy modelling examples
.......................... 97
4.5.1
Thermistor modelling
............................. 97
4.5.2
Time series modelling
............................. 99
5.
Parsimonious neurofuzzy modelling
.......................103
5.1
Iterative construction modelling
..........................103
5.2
Additive neurofuzzy modelling algorithms
.................106
5.3
Adaptive spline modelling algorithm (ASMOD)
............107
5.3.1
ASMOD refinements
..............................107
5.3.2
Illustrative examples of ASMOD
...................
Ill
5.4
Extended additive neurofuzzy models
.....................119
5.4.1
Weight identification
.............................. 122
5.4.2
Extended additive model structure identification
..... 124
5.5
Hierarchical neurofuzzy models
........................... 125
5.6
Regularised neurofuzzy models
........................... 129
5.6.1
Bayesian
régularisation
............................129
5.6.2
Error bars
.......................................132
5.6.3
Priors for neurofuzzy models
.......................133
5.6.4
Local regularised neurofuzzy models
................136
5.7
Complexity reduction through orthogonal least squares
......143
5.8
A-optimality neurofuzzy model construction (NeuDec)
......144
6.
Local neurofuzzy modelling
...............................153
6.1
Introduction
...........................................153
6.2
Local orthogonal partitioning algorithms
..................157
6.2.1
к
-
d
Trees
......................................157
6.2.2
Quad-trees
......................................161
Contents
XV
6.3
Operating point dependent neurofuzzy models
.............164
6.4
State space representations of operating point dependent
neurofuzzy models
......................................168
6.5
Mixture of experts modelling
.............................173
6.6
Multi-input-Multi-output
(MIMO)
modelling
via input variable selection
..............................187
6.6.1
MIMO NARX
neurofuzzy model decomposition
......187
6.6.2
Feedforward Gram-Schmidt OLS procedure
for linear systems
.................................191
6.6.3
Input variable selection via the modified Gram-Schmidt
OLS for piecewise linear submodels
.................193
7.
Delaunay input space partitioning modelling
..............201
7.1
Introduction
...........................................201
7.2
Delaunay
triangulation
of the input space
.................202
7.3
Delaunay input space partitioning for locally linear models
.. 204
7.4
The
Bézier-Bernstein
modelling network
..................209
7.4.1
Neurofuzzy modelling using
Bézier-Bernstein
function for univariate term fi(xi) and bi
variate
term fi^Xi^Xjy)
...............................209
7.4.2
The complete
Bézier-Bernstein
model construction
algorithm
.......................................219
7.4.3
Numerical examples
..............................220
8.
Neurofuzzy linearisation modelling
for nonlinear state estimation
.............................225
8.1
Introduction to linearisation modelling
....................225
8.2
Neurofuzzy local linearisation and the MASMOD algorithm
.. 228
8.3
A hybrid learning scheme combining MASMOD
and EM algorithms for neurofuzzy local linearisation
........236
8.4
Neurofuzzy feedback linearisation (NFFL)
.................239
8.5
Formulation of neurofuzzy state estimators
................245
8.6
An example of nonlinear trajectory estimation
.............249
9.
Multisensor data fusion using
Kalman
filters based
on neurofuzzy linearisation
...............................255
9.1
Introduction
...........................................255
9.2
Measurement fusion
....................................258
9.2.1
Outputs augmented fusion (OAF)
..................259
9.2.2
Optimal weighting measurement fusion (OWMF)
.....259
9.2.3
On functional equivalence of OAF and OWMF
.......260
9.2.4
On the decentralised architecture
...................262
9.3
State-vector fusion
......................................263
9.3.1
State-vector assimilation fusion (SVAF)
.............263
9.3.2
Track-to-track fusion (TTF)
.......................264
XVI Contents
9.3.3
On the decentralised architecture
...................265
9.4
Hierarchical multisensor data fusion
-
trade-off between centralised and decentralised Architectures
. 266
9.5
Simulation examples
....................................267
9.5.1
On functional equivalence of two measurement fusion
methods
........................................267
9.5.2
On hierarchical multisensor data fusion
.............271
10.
Support vector neurofuzzy models
........................281
10.1
Introduction
...........................................281
10.2
Support vector machines
................................282
10.2.1
Loss functions
...................................284
10.2.2
Feature space and kernel functions
..................284
10.3
Support vector regression
................................286
10.4
Support vector neurofuzzy networks
......................289
10.5
SUPANOVA
...........................................297
10.6
A comparison among neural network models
...............303
10.7
Conclusions
............................................304
References
....................................................307
Index
.........................................................319
|
| any_adam_object | 1 |
| author | Harris, Christopher J. Hong, Xia Gan, Qiang 1962- |
| author_GND | (DE-588)123662087 |
| author_facet | Harris, Christopher J. Hong, Xia Gan, Qiang 1962- |
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| author_sort | Harris, Christopher J. |
| author_variant | c j h cj cjh x h xh q g qg |
| building | Verbundindex |
| bvnumber | BV023788967 |
| classification_rvk | SK 950 |
| ctrlnum | (OCoLC)248137322 (DE-599)BVBBV023788967 |
| dewey-full | 003.01511322 006.32 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 003 - Systems 006 - Special computer methods |
| dewey-raw | 003.01511322 006.32 |
| dewey-search | 003.01511322 006.32 |
| dewey-sort | 13.01511322 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Mathematik |
| format | Book |
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| id | DE-604.BV023788967 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T21:36:50Z |
| institution | BVB |
| isbn | 3540426868 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017431174 |
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| physical | XVI, 323 S. graph. Darst. |
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| publisher | Springer |
| record_format | marc |
| series2 | Advanced information processing |
| spelling | Harris, Christopher J. Verfasser aut Adaptive modelling, estimation and fusion from data a neurofuzzy approach Chris Harris ; Xia Hong ; Qiang Gan Berlin [u.a.] Springer 2002 XVI, 323 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Advanced information processing Neuro-Fuzzy-System (DE-588)4560677-8 gnd rswk-swf Adaptive Schätzung (DE-588)4342646-3 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Nichtlineares dynamisches System (DE-588)4126142-2 gnd rswk-swf Datenfusion (DE-588)4582612-2 gnd rswk-swf Nichtlineares dynamisches System (DE-588)4126142-2 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Neuro-Fuzzy-System (DE-588)4560677-8 s Datenfusion (DE-588)4582612-2 s Adaptive Schätzung (DE-588)4342646-3 s Hong, Xia Verfasser aut Gan, Qiang 1962- Verfasser (DE-588)123662087 aut Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017431174&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Harris, Christopher J. Hong, Xia Gan, Qiang 1962- Adaptive modelling, estimation and fusion from data a neurofuzzy approach Neuro-Fuzzy-System (DE-588)4560677-8 gnd Adaptive Schätzung (DE-588)4342646-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Nichtlineares dynamisches System (DE-588)4126142-2 gnd Datenfusion (DE-588)4582612-2 gnd |
| subject_GND | (DE-588)4560677-8 (DE-588)4342646-3 (DE-588)4193754-5 (DE-588)4126142-2 (DE-588)4582612-2 |
| title | Adaptive modelling, estimation and fusion from data a neurofuzzy approach |
| title_auth | Adaptive modelling, estimation and fusion from data a neurofuzzy approach |
| title_exact_search | Adaptive modelling, estimation and fusion from data a neurofuzzy approach |
| title_full | Adaptive modelling, estimation and fusion from data a neurofuzzy approach Chris Harris ; Xia Hong ; Qiang Gan |
| title_fullStr | Adaptive modelling, estimation and fusion from data a neurofuzzy approach Chris Harris ; Xia Hong ; Qiang Gan |
| title_full_unstemmed | Adaptive modelling, estimation and fusion from data a neurofuzzy approach Chris Harris ; Xia Hong ; Qiang Gan |
| title_short | Adaptive modelling, estimation and fusion from data |
| title_sort | adaptive modelling estimation and fusion from data a neurofuzzy approach |
| title_sub | a neurofuzzy approach |
| topic | Neuro-Fuzzy-System (DE-588)4560677-8 gnd Adaptive Schätzung (DE-588)4342646-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Nichtlineares dynamisches System (DE-588)4126142-2 gnd Datenfusion (DE-588)4582612-2 gnd |
| topic_facet | Neuro-Fuzzy-System Adaptive Schätzung Maschinelles Lernen Nichtlineares dynamisches System Datenfusion |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017431174&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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