Computational intelligence: methods and techniques
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English Polish |
| Published: |
Berlin [u.a.]
Springer
2008
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Inhaltsverzeichnis |
| Item Description: | Aus dem Poln. übers. |
| Physical Description: | XIII, 514 S. zahlr. graph. Darst. 235 mm x 155 mm |
| ISBN: | 3540762876 9783540762874 |
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MARC
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| 100 | 1 | |a Rutkowski, Leszek |d 1952- |e Verfasser |0 (DE-588)1052609511 |4 aut | |
| 240 | 1 | 0 | |a Metody i techniki sztucznej inteligencji |
| 245 | 1 | 0 | |a Computational intelligence |b methods and techniques |c Leszek Rutkowski |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2008 | |
| 300 | |a XIII, 514 S. |b zahlr. graph. Darst. |c 235 mm x 155 mm | ||
| 336 | |b txt |2 rdacontent | ||
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Record in the Search Index
| _version_ | 1804137506543763456 |
|---|---|
| adam_text | Contents
Foreword
v
1
Introduction
1
2
Selected issues of artificial intelligence
7
2.1
Introduction
........................... 7
2.2
An outline of artificial intelligence history
.......... 8
2.3
Expert systems
......................... 10
2.4
Robotics
............................. 11
2.5
Processing of speech and natural language
.......... 13
2.6
Heuristics and research strategies
............... 15
2.7
Cognitivistics
.......................... 16
2.8
Intelligence of ants
....................... 17
2.9
Artificial life
........................... 19
2.10
Bots
............................... 20
2.11
Perspectives of artificial
intelligence
development
...... 22
2.12
Notes
.............................. 23
3
Methods of knowledge representation using rough sets
25
3.1
Introduction
...........................
2ő
3.2
Basic terms
........................... 27
3.3
Set approximation
....................... 34
3.4
Approximation of family of sets
................ 14
3.5
Analysis of decision tables
................... 46
Contents
3.6 Application
of
LERS
software.................
54
3.7 Notes .............................. 61
Methods of knowledge representation using type-1
fuzzy sets
63
4.1
Introduction
........................... 63
4.2
Basic terms and definitions of fuzzy sets theory
....... 63
4.3
Operations on fuzzy sets
.................... 76
4.4
The extension principle
.................... 83
4.5
Fuzzy numbers
......................... 87
4.6
Triangular norms and negations
................ 96
4.7
Fuzzy relations and their properties
............. 108
4.8
Approximate reasoning
..................... 112
4.8.1
Basic rules of inference in binary logic
........ 112
4.8.2
Basic rules of inference in fuzzy logic
......... 114
4.8.3
Inference rules for the Mamdani model
........ 118
4.8.4
Inference rules for the logical model
......... 119
4.9
Fuzzy inference systems
.................... 122
4.9.1
Rules base
........................ 123
4.9.2
Fuzzification block
................... 124
4.9.3
Inference block
..................... 125
4.9.4
Denazification block
.................. 131
4.10
Application of fuzzy sets
.................... 134
4.10.1
Fuzzy Delphi method
................. 134
4.10.2
Weighted fuzzy Delphi method
............ 138
4.10.3
Fuzzy PERT method
.................. 139
4.10.4
Decision making in a fuzzy environment
....... 142
4.11
Notes
.............................. 153
Methods of knowledge representation using type-2
fuzzy sets
155
•5.1
Introduction
........................... 155
5.2
Basic definitions
........................ 156
5.3
Footprint of uncertainty
.................... 160
5.4
Embedded fuzzy sets
...................... 162
5.5
Basic operations on type-2 fuzzy sets
............. 164
5.6
Type-2 fuzzy relations
..................... 169
5.7
Type reduction
......................... 172
5.8
Type-2 fuzzy inference systems
................ 178
5.8.1
Fuzzification block
................... 178
5.8.2
Rules base
........................ 180
5.5.3 Inference block
..................... 180
5.9
Notos
..............................186
Contents xi
Neural
networks
and their learning algorithms
187
6.1
Introduction
........................... 187
6.2
Neuron and its models
..................... 188
6.2.1
Structure and functioning of a single neuron
..... 188
6.2.2
Perceptron
....................... 190
6.2.3
Adaline model
..................... 196
6.2.4
Sigmoidal neuron model
................ 202
6.2.5
Hebb neuron model
.................. 206
6.3
Multilayer feed-forward networks
............... 208
6.3.1
Structure and functioning of the network
...... 208
6.3.2
Backpropagation algorithm
.............. 210
6.3.3
Backpropagation algorithm with momentum term
. 218
6.3.4
Variable-metric algorithm
............... 220
6.3.5
Levenberg-Marquardt algorithm
........... 221
6.3.6
Recursive least squares method
............ 222
6.3.7
Selection of network architecture
........... 225
6.4
Recurrent neural networks
................... 232
6.4.1
Hopfield neural network
................ 232
6.4.2
Hamming neural network
............... 236
6.4.3
Multilayer neural networks with feedback
...... 238
6.4.4
BAM network
...................... 238
6.5
Self-organizing neural networks with competitive learning
. 240
6.5.1
WTA neural networks
................. 240
6.5.2
WTM neural networks
................. 246
6.6
ART neural networks
...................... 250
6.7
Radial-basis function networks
................ 254
6.8
Probabilistic neural networks
................. 261
6.9
Notes
.............................. 263
Evolutionary algorithms
265
7.1
Introduction
........................... 265
7.2
Optimization problems and evolutionary algorithms
.... 266
7.3
Type of algorithms classified as evolutionary algorithms
. . 267
7.3.1
Classical genetic algorithm
.............. 268
7.3.2
Evolution strategies
.................. 289
7.3.3
Evolutionary programming
.............. 307
7.3.4
Genetic programming
................. 309
7.4
Advanced techniques in evolutionary algorithms
....... 310
7.4.1
Exploration and exploitation
............. 310
7.4.2
Selection methods
................... 311
7.4.3
Scaling the fitness function
.............. 314
7.4.4
Specific reproduction procedures
........... 315
7.4.5
Coding methods
.................... 317
7.4.6
Types of crossover
................... 320
xii Contents
7.4.7
Types of mutation
................... 322
7.4.8
Inversion
........................ 323
7.5
Evolutionary algorithms in the designing of neural networks
323
7.5.1
Evolutionary algorithms applied to the learning
of weights of neural networks
............. 324
7.5.2
Evolutionary algorithms for determining
the topology of the neural network
.......... 327
7.5.3
Evolutionary algorithms for learning weights
and determining the topology of the neural network
330
7.6
Evolutionary algorithms vs fuzzy systems
.......... 332
7.6.1
Fuzzy systems for evolution control
.......... 333
7.6.2
Evolution of fuzzy systems
.............. 335
7.7
Notes
.............................. 344
8
Data clustering methods
349
8.1
Introduction
........................... 349
8.2
Hard and fuzzy partitions
................... 350
8.3
Distance measures
....................... 354
8.4
HCM
algorithm
......................... 357
8.5
FCM algorithm
......................... 359
8.6
PCM algorithm
......................... 360
8.7
Gustafson-Kessel algorithm
.................. 361
8.8
FMLE algorithm
........................ 363
8.9
Clustering validity measures
.................. 364
8.10
Illustration of operation of data clustering algorithms
.... 367
8.11
Notes
.............................. 369
9
Neuro-fuzzy systems of Mamdani, logical
and Takagi-Sugeno type
371
9.1
Introduction
........................... 371
9.2
Description of simulation problems used
........... 372
9.2.1
Polymerization
..................... 372
9.2.2
Modeling a static non-linear function
......... 373
9.2.3
Modeling a non-linear dynamic object (Nonlinear
Dynamic Problem
-
NDP)
............... 373
9.2.4
Modeling the taste of rice
............... 374
9.2.5
Distinguishing of the brand of wine
.......... 374
9.2.6
Classification of iris flower
............... 374
9.3
Neuro-fuzzy systems of Mamdani type
............ 375
9.3.1
A-type
systems
..................... 375
9.3.2
B-type systems
..................... 377
9.3.3
Mamdani type systems in modeling problems
.... 378
9.4
Neuro-fuzzy systems of logical type
.............. 390
9.4.1
Ml-type systems
.................... 392
9.4.2
M2-type systems
.................... 399
9.4.3
МЗ
-type
systems
.................... 405
Contents xiii
9.5
Neuro-fuzzy systems of Takagi-Sugeno type
......... 410
9.5.1
Ml-type systems
.................... 413
9.5.2
M2-type systems
.................... 414
9.5.3
МЗ
-type
systems
.................... 416
9.6
Learning algorithms of neuro-fuzzy systems
......... 418
9.7
Comparison of neuro-fuzzy systems
.............. 435
9.7.1
Models evaluation criteria taking into account
their complexity
.................... 437
9.7.2
Criteria isolines method
................ 439
9.8
Notes
.............................. 448
10
Flexible neuro-fuzzy systems
449
10.1
Introduction
........................... 449
10.2
Soft triangular norms
..................... 449
10.3
Parameterized triangular norms
................ 452
10.4
Adjustable triangular norms
.................. 456
10.5
Flexible systems
........................ 461
10.6
Learning algorithms
...................... 463
10.6.1
Basic operators
..................... 470
10.6.2
Membership functions
................. 471
10.6.3
Constraints
....................... 473
10.6.4
ď-functions
....................... 473
10.7
Simulation examples
...................... 479
10.7.1
Polymerization
..................... 480
10.7.2
Modeling the taste of rice
............... 480
10.7.3
Classification of iris flower
............... 482
10.7.4
Classification of wine
.................. 484
10.8
Notes
.............................. 492
References
495
|
| adam_txt |
Contents
Foreword
v
1
Introduction
1
2
Selected issues of artificial intelligence
7
2.1
Introduction
. 7
2.2
An outline of artificial intelligence history
. 8
2.3
Expert systems
. 10
2.4
Robotics
. 11
2.5
Processing of speech and natural language
. 13
2.6
Heuristics and research strategies
. 15
2.7
Cognitivistics
. 16
2.8
Intelligence of ants
. 17
2.9
Artificial life
. 19
2.10
Bots
. 20
2.11
Perspectives of artificial
intelligence
development
. 22
2.12
Notes
. 23
3
Methods of knowledge representation using rough sets
25
3.1
Introduction
.
2ő
3.2
Basic terms
. 27
3.3
Set approximation
. 34
3.4
Approximation of family of sets
. 14
3.5
Analysis of decision tables
. 46
Contents
3.6 Application
of
LERS
software.
54
3.7 Notes . 61
Methods of knowledge representation using type-1
fuzzy sets
63
4.1
Introduction
. 63
4.2
Basic terms and definitions of fuzzy sets theory
. 63
4.3
Operations on fuzzy sets
. 76
4.4
The extension principle
. 83
4.5
Fuzzy numbers
. 87
4.6
Triangular norms and negations
. 96
4.7
Fuzzy relations and their properties
. 108
4.8
Approximate reasoning
. 112
4.8.1
Basic rules of inference in binary logic
. 112
4.8.2
Basic rules of inference in fuzzy logic
. 114
4.8.3
Inference rules for the Mamdani model
. 118
4.8.4
Inference rules for the logical model
. 119
4.9
Fuzzy inference systems
. 122
4.9.1
Rules base
. 123
4.9.2
Fuzzification block
. 124
4.9.3
Inference block
. 125
4.9.4
Denazification block
. 131
4.10
Application of fuzzy sets
. 134
4.10.1
Fuzzy Delphi method
. 134
4.10.2
Weighted fuzzy Delphi method
. 138
4.10.3
Fuzzy PERT method
. 139
4.10.4
Decision making in a fuzzy environment
. 142
4.11
Notes
. 153
Methods of knowledge representation using type-2
fuzzy sets
155
•5.1
Introduction
. 155
5.2
Basic definitions
. 156
5.3
Footprint of uncertainty
. 160
5.4
Embedded fuzzy sets
. 162
5.5
Basic operations on type-2 fuzzy sets
. 164
5.6
Type-2 fuzzy relations
. 169
5.7
Type reduction
. 172
5.8
Type-2 fuzzy inference systems
. 178
5.8.1
Fuzzification block
. 178
5.8.2
Rules base
. 180
5.5.3 Inference block
. 180
5.9
Notos
.186
Contents xi
Neural
networks
and their learning algorithms
187
6.1
Introduction
. 187
6.2
Neuron and its models
. 188
6.2.1
Structure and functioning of a single neuron
. 188
6.2.2
Perceptron
. 190
6.2.3
Adaline model
. 196
6.2.4
Sigmoidal neuron model
. 202
6.2.5
Hebb neuron model
. 206
6.3
Multilayer feed-forward networks
. 208
6.3.1
Structure and functioning of the network
. 208
6.3.2
Backpropagation algorithm
. 210
6.3.3
Backpropagation algorithm with momentum term
. 218
6.3.4
Variable-metric algorithm
. 220
6.3.5
Levenberg-Marquardt algorithm
. 221
6.3.6
Recursive least squares method
. 222
6.3.7
Selection of network architecture
. 225
6.4
Recurrent neural networks
. 232
6.4.1
Hopfield neural network
. 232
6.4.2
Hamming neural network
. 236
6.4.3
Multilayer neural networks with feedback
. 238
6.4.4
BAM network
. 238
6.5
Self-organizing neural networks with competitive learning
. 240
6.5.1
WTA neural networks
. 240
6.5.2
WTM neural networks
. 246
6.6
ART neural networks
. 250
6.7
Radial-basis function networks
. 254
6.8
Probabilistic neural networks
. 261
6.9
Notes
. 263
Evolutionary algorithms
265
7.1
Introduction
. 265
7.2
Optimization problems and evolutionary algorithms
. 266
7.3
Type of algorithms classified as evolutionary algorithms
. . 267
7.3.1
Classical genetic algorithm
. 268
7.3.2
Evolution strategies
. 289
7.3.3
Evolutionary programming
. 307
7.3.4
Genetic programming
. 309
7.4
Advanced techniques in evolutionary algorithms
. 310
7.4.1
Exploration and exploitation
. 310
7.4.2
Selection methods
. 311
7.4.3
Scaling the fitness function
. 314
7.4.4
Specific reproduction procedures
. 315
7.4.5
Coding methods
. 317
7.4.6
Types of crossover
. 320
xii Contents
7.4.7
Types of mutation
. 322
7.4.8
Inversion
. 323
7.5
Evolutionary algorithms in the designing of neural networks
323
7.5.1
Evolutionary algorithms applied to the learning
of weights of neural networks
. 324
7.5.2
Evolutionary algorithms for determining
the topology of the neural network
. 327
7.5.3
Evolutionary algorithms for learning weights
and determining the topology of the neural network
330
7.6
Evolutionary algorithms vs fuzzy systems
. 332
7.6.1
Fuzzy systems for evolution control
. 333
7.6.2
Evolution of fuzzy systems
. 335
7.7
Notes
. 344
8
Data clustering methods
349
8.1
Introduction
. 349
8.2
Hard and fuzzy partitions
. 350
8.3
Distance measures
. 354
8.4
HCM
algorithm
. 357
8.5
FCM algorithm
. 359
8.6
PCM algorithm
. 360
8.7
Gustafson-Kessel algorithm
. 361
8.8
FMLE algorithm
. 363
8.9
Clustering validity measures
. 364
8.10
Illustration of operation of data clustering algorithms
. 367
8.11
Notes
. 369
9
Neuro-fuzzy systems of Mamdani, logical
and Takagi-Sugeno type
371
9.1
Introduction
. 371
9.2
Description of simulation problems used
. 372
9.2.1
Polymerization
. 372
9.2.2
Modeling a static non-linear function
. 373
9.2.3
Modeling a non-linear dynamic object (Nonlinear
Dynamic Problem
-
NDP)
. 373
9.2.4
Modeling the taste of rice
. 374
9.2.5
Distinguishing of the brand of wine
. 374
9.2.6
Classification of iris flower
. 374
9.3
Neuro-fuzzy systems of Mamdani type
. 375
9.3.1
A-type
systems
. 375
9.3.2
B-type systems
. 377
9.3.3
Mamdani type systems in modeling problems
. 378
9.4
Neuro-fuzzy systems of logical type
. 390
9.4.1
Ml-type systems
. 392
9.4.2
M2-type systems
. 399
9.4.3
МЗ
-type
systems
. 405
Contents xiii
9.5
Neuro-fuzzy systems of Takagi-Sugeno type
. 410
9.5.1
Ml-type systems
. 413
9.5.2
M2-type systems
. 414
9.5.3
МЗ
-type
systems
. 416
9.6
Learning algorithms of neuro-fuzzy systems
. 418
9.7
Comparison of neuro-fuzzy systems
. 435
9.7.1
Models evaluation criteria taking into account
their complexity
. 437
9.7.2
Criteria isolines method
. 439
9.8
Notes
. 448
10
Flexible neuro-fuzzy systems
449
10.1
Introduction
. 449
10.2
Soft triangular norms
. 449
10.3
Parameterized triangular norms
. 452
10.4
Adjustable triangular norms
. 456
10.5
Flexible systems
. 461
10.6
Learning algorithms
. 463
10.6.1
Basic operators
. 470
10.6.2
Membership functions
. 471
10.6.3
Constraints
. 473
10.6.4
ď-functions
. 473
10.7
Simulation examples
. 479
10.7.1
Polymerization
. 480
10.7.2
Modeling the taste of rice
. 480
10.7.3
Classification of iris flower
. 482
10.7.4
Classification of wine
. 484
10.8
Notes
. 492
References
495 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Rutkowski, Leszek 1952- |
| author_GND | (DE-588)1052609511 |
| author_facet | Rutkowski, Leszek 1952- |
| author_role | aut |
| author_sort | Rutkowski, Leszek 1952- |
| author_variant | l r lr |
| building | Verbundindex |
| bvnumber | BV023219828 |
| classification_rvk | ST 300 ST 301 ST 515 |
| ctrlnum | (OCoLC)244018307 (DE-599)DNB985757639 |
| 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 |
| discipline_str_mv | Informatik |
| format | Book |
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| id | DE-604.BV023219828 |
| illustrated | Illustrated |
| index_date | 2024-07-02T20:15:32Z |
| indexdate | 2024-07-09T21:13:23Z |
| institution | BVB |
| isbn | 3540762876 9783540762874 |
| language | English Polish |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016405756 |
| oclc_num | 244018307 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-703 DE-473 DE-BY-UBG DE-634 DE-573 DE-11 DE-91 DE-BY-TUM |
| owner_facet | DE-355 DE-BY-UBR DE-703 DE-473 DE-BY-UBG DE-634 DE-573 DE-11 DE-91 DE-BY-TUM |
| physical | XIII, 514 S. zahlr. graph. Darst. 235 mm x 155 mm |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Springer |
| record_format | marc |
| spelling | Rutkowski, Leszek 1952- Verfasser (DE-588)1052609511 aut Metody i techniki sztucznej inteligencji Computational intelligence methods and techniques Leszek Rutkowski Berlin [u.a.] Springer 2008 XIII, 514 S. zahlr. graph. Darst. 235 mm x 155 mm txt rdacontent n rdamedia nc rdacarrier Aus dem Poln. übers. Soft Computing (DE-588)4455833-8 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 s DE-604 Soft Computing (DE-588)4455833-8 s Erscheint auch als Online-Ausgabe 978-3-540-76288-1 http://d-nb.info/985757639/04 Inhaltsverzeichnis Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016405756&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Rutkowski, Leszek 1952- Computational intelligence methods and techniques Soft Computing (DE-588)4455833-8 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4455833-8 (DE-588)4033447-8 |
| title | Computational intelligence methods and techniques |
| title_alt | Metody i techniki sztucznej inteligencji |
| title_auth | Computational intelligence methods and techniques |
| title_exact_search | Computational intelligence methods and techniques |
| title_exact_search_txtP | Computational intelligence methods and techniques |
| title_full | Computational intelligence methods and techniques Leszek Rutkowski |
| title_fullStr | Computational intelligence methods and techniques Leszek Rutkowski |
| title_full_unstemmed | Computational intelligence methods and techniques Leszek Rutkowski |
| title_short | Computational intelligence |
| title_sort | computational intelligence methods and techniques |
| title_sub | methods and techniques |
| topic | Soft Computing (DE-588)4455833-8 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Soft Computing Künstliche Intelligenz |
| url | http://d-nb.info/985757639/04 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016405756&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT rutkowskileszek metodyitechnikisztucznejinteligencji AT rutkowskileszek computationalintelligencemethodsandtechniques |
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