The Resource Computational Intelligence Systems and Applications : Neuro-Fuzzy and Fuzzy Neural Synergisms, by Marian B. Gorzalczany, (electronic resource)

Computational Intelligence Systems and Applications : Neuro-Fuzzy and Fuzzy Neural Synergisms, by Marian B. Gorzalczany, (electronic resource)

Label
Computational Intelligence Systems and Applications : Neuro-Fuzzy and Fuzzy Neural Synergisms
Title
Computational Intelligence Systems and Applications
Title remainder
Neuro-Fuzzy and Fuzzy Neural Synergisms
Statement of responsibility
by Marian B. Gorzalczany
Creator
Author
Author
Subject
Language
  • eng
  • eng
Summary
This book presents new concepts and implementations of Computational Intelligence (CI) systems (based on neuro-fuzzy and fuzzy neural synergisms) and a broad comparative analysis with the best-known existing neuro-fuzzy systems as well as with systems representing other knowledge-discovery techniques such as rough sets, decision trees, regression trees, probabilistic rule induction etc. This presentation is preceded by a discussion of the main directions of synthesizing fuzzy sets, artificial neural networks and genetic algorithms in the framework of designing CI systems. In order to keep the book self-contained, introductions to the basic concepts of fuzzy systems, artificial neural networks and genetic algorithms are given. This book is intended for researchers and practitioners in AI/CI fields and for students of computer science or neighbouring areas
Member of
http://library.link/vocab/creatorName
Gorzalczany, Marian B
Dewey number
006.3
http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsaut
K0fGjhR2q7I
Image bit depth
0
Language note
English
LC call number
Q334-342
Literary form
non fiction
Nature of contents
dictionaries
Series statement
Studies in Fuzziness and Soft Computing,
Series volume
86
http://library.link/vocab/subjectName
  • Artificial intelligence
  • Computer science
  • Computer science
  • Artificial Intelligence
  • Computational Mathematics and Numerical Analysis
  • Computational Science and Engineering
Label
Computational Intelligence Systems and Applications : Neuro-Fuzzy and Fuzzy Neural Synergisms, by Marian B. Gorzalczany, (electronic resource)
Instantiates
Publication
Note
"With 147 figures and 21 tables."
Antecedent source
mixed
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Color
not applicable
Content category
text
Content type code
  • txt
Contents
1 Introduction -- 1.1 A general concept of computational intelligence -- 1.2 The building blocks of computational intelligence systems -- 1.3 Objectives and scope of this book -- 2 Elements of the theory of fuzzy sets -- 2.1 Basic notions, operations on fuzzy sets, and fuzzy relations -- 2.2 Fuzzy inference systems -- 3 Essentials of artificial neural networks -- 3.1 Processing elements and multilayer perceptrons -- 3.2 Radial basis function networks -- 4 Brief introduction to genetic algorithms -- 4.1 Basic components of genetic algorithms -- 4.2 Theoretical introduction to genetic computing -- 5 Main directions of combining artificial neural networks, fuzzy sets and evolutionary computations in designing computational intelligence systems -- 5.1 Artificial intelligence versus computational intelligence -- 5.2 Designing computational intelligence systems -- 5.3 Selected neuro-fuzzy systems -- 6 Neuro-fuzzy(-genetic) system for synthesizing rule-based knowledge from data -- 6.1 Synthesizing rule-based knowledge from data — statement of the problem -- 6.2 Neuro-fuzzy system in learning mode — problem of knowledge acquisition -- 6.3 Neuro-fuzzy system in inference mode — approximate inference engine -- 6.4 Learning techniques -- 6.5 A numerical example of synthesizing rule-based knowledge from data — modelling the Mackey-Glass chaotic time series -- 6.6 Synthesizing rule-based knowledge from “fish data” -- 7 Rule-based neuro-fuzzy modelling of dynamic systems and designing of controllers -- 7.1 System identification — statement of the problem and its general solution in the framework of neuro-fuzzy methodology -- 7.2 Rule-based neuro-fuzzy modelling of an industrial gas furnace system -- 7.3 Designing the neuro-fuzzy controller for a simulated backing up of a truck -- 8 Neuro-fuzzy(-genetic) rule-based classifier designed from data for intelligent decision support -- 8.1 Designing the classifier from data — statement of the problem -- 8.2 Learning mode of neuro-fuzzy classifier -- 8.3 Inference (decision making) mode of neuro-fuzzy classifier -- 8.4 Neuro-fuzzy decision support system for diagnosing breast cancer -- 8.5 Neuro-fuzzy-genetic decision support system for the glass identification problem (forensic science) -- 8.6 Neuro-fuzzy-genetic decision support system for determining the age of abalone (marine biology) -- 9 Fuzzy neural network for system modelling and control -- 9.1 Learning mode of the network -- 9.2 Inference mode of the network -- 9.3 Fuzzy neural modelling of dynamic systems (an industrial gas furnace system) -- 9.4 Fuzzy neural controller -- 10 Fuzzy neural classifier -- 10.1 Learning and inference modes of the classifier -- 10.2 Fuzzy neural classifier for diagnosis of surgical cases in the domain of equine colic -- A Appendices -- A.1.1 Inputs -- A.1.2 Output -- A.2.1 Inputs -- A.2.2 Outputs — set of two class labels -- A.3.1 Inputs -- A.3.2 Outputs — set of two class labels -- A.4.1 Inputs -- A.4.2 Outputs — set of three class labels -- A.5.1 Inputs -- A.5.2 Outputs — three sets of class labels -- References
Dimensions
unknown
Edition
1st ed. 2002.
Extent
1 online resource (X, 364 p.)
File format
multiple file formats
Form of item
online
Isbn
9783790818017
Level of compression
uncompressed
Media category
computer
Media type code
  • c
Other control number
10.1007/978-3-7908-1801-7
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
  • (CKB)3400000000111114
  • (SSID)ssj0001007573
  • (PQKBManifestationID)11587855
  • (PQKBTitleCode)TC0001007573
  • (PQKBWorkID)10952067
  • (PQKB)10929254
  • (DE-He213)978-3-7908-1801-7
  • (MiAaPQ)EBC3100821
  • (EXLCZ)993400000000111114
Label
Computational Intelligence Systems and Applications : Neuro-Fuzzy and Fuzzy Neural Synergisms, by Marian B. Gorzalczany, (electronic resource)
Publication
Note
"With 147 figures and 21 tables."
Antecedent source
mixed
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Color
not applicable
Content category
text
Content type code
  • txt
Contents
1 Introduction -- 1.1 A general concept of computational intelligence -- 1.2 The building blocks of computational intelligence systems -- 1.3 Objectives and scope of this book -- 2 Elements of the theory of fuzzy sets -- 2.1 Basic notions, operations on fuzzy sets, and fuzzy relations -- 2.2 Fuzzy inference systems -- 3 Essentials of artificial neural networks -- 3.1 Processing elements and multilayer perceptrons -- 3.2 Radial basis function networks -- 4 Brief introduction to genetic algorithms -- 4.1 Basic components of genetic algorithms -- 4.2 Theoretical introduction to genetic computing -- 5 Main directions of combining artificial neural networks, fuzzy sets and evolutionary computations in designing computational intelligence systems -- 5.1 Artificial intelligence versus computational intelligence -- 5.2 Designing computational intelligence systems -- 5.3 Selected neuro-fuzzy systems -- 6 Neuro-fuzzy(-genetic) system for synthesizing rule-based knowledge from data -- 6.1 Synthesizing rule-based knowledge from data — statement of the problem -- 6.2 Neuro-fuzzy system in learning mode — problem of knowledge acquisition -- 6.3 Neuro-fuzzy system in inference mode — approximate inference engine -- 6.4 Learning techniques -- 6.5 A numerical example of synthesizing rule-based knowledge from data — modelling the Mackey-Glass chaotic time series -- 6.6 Synthesizing rule-based knowledge from “fish data” -- 7 Rule-based neuro-fuzzy modelling of dynamic systems and designing of controllers -- 7.1 System identification — statement of the problem and its general solution in the framework of neuro-fuzzy methodology -- 7.2 Rule-based neuro-fuzzy modelling of an industrial gas furnace system -- 7.3 Designing the neuro-fuzzy controller for a simulated backing up of a truck -- 8 Neuro-fuzzy(-genetic) rule-based classifier designed from data for intelligent decision support -- 8.1 Designing the classifier from data — statement of the problem -- 8.2 Learning mode of neuro-fuzzy classifier -- 8.3 Inference (decision making) mode of neuro-fuzzy classifier -- 8.4 Neuro-fuzzy decision support system for diagnosing breast cancer -- 8.5 Neuro-fuzzy-genetic decision support system for the glass identification problem (forensic science) -- 8.6 Neuro-fuzzy-genetic decision support system for determining the age of abalone (marine biology) -- 9 Fuzzy neural network for system modelling and control -- 9.1 Learning mode of the network -- 9.2 Inference mode of the network -- 9.3 Fuzzy neural modelling of dynamic systems (an industrial gas furnace system) -- 9.4 Fuzzy neural controller -- 10 Fuzzy neural classifier -- 10.1 Learning and inference modes of the classifier -- 10.2 Fuzzy neural classifier for diagnosis of surgical cases in the domain of equine colic -- A Appendices -- A.1.1 Inputs -- A.1.2 Output -- A.2.1 Inputs -- A.2.2 Outputs — set of two class labels -- A.3.1 Inputs -- A.3.2 Outputs — set of two class labels -- A.4.1 Inputs -- A.4.2 Outputs — set of three class labels -- A.5.1 Inputs -- A.5.2 Outputs — three sets of class labels -- References
Dimensions
unknown
Edition
1st ed. 2002.
Extent
1 online resource (X, 364 p.)
File format
multiple file formats
Form of item
online
Isbn
9783790818017
Level of compression
uncompressed
Media category
computer
Media type code
  • c
Other control number
10.1007/978-3-7908-1801-7
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
  • (CKB)3400000000111114
  • (SSID)ssj0001007573
  • (PQKBManifestationID)11587855
  • (PQKBTitleCode)TC0001007573
  • (PQKBWorkID)10952067
  • (PQKB)10929254
  • (DE-He213)978-3-7908-1801-7
  • (MiAaPQ)EBC3100821
  • (EXLCZ)993400000000111114

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