Fuzzy neural network theory and application /:
This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. S...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
River Edge, NJ :
World Scientific,
2004.
|
| Schriftenreihe: | Series in machine perception and artificial intelligence ;
v. 59. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. Special emphasis is placed on the fundamental concepts and architecture analysis of fuzzy neural networks. The book is unique in treating all kinds of fuzzy neural networks and their learning algorithms and universal approximations, and employing simulation examples which are carefully designed to he. |
| Beschreibung: | 1 online resource (xvii, 376 pages) |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9789812794215 9812794212 |
Internformat
MARC
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| 100 | 1 | |a Liu, Puyin. |0 http://id.loc.gov/authorities/names/nb2004305717 | |
| 245 | 1 | 0 | |a Fuzzy neural network theory and application / |c Puyin Liu, Hongxing Li. |
| 260 | |a River Edge, NJ : |b World Scientific, |c 2004. | ||
| 300 | |a 1 online resource (xvii, 376 pages) | ||
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| 490 | 1 | |a Series in machine perception and artificial intelligence ; |v v. 59 | |
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Foreword; Preface; Chapter I Introduction; S1.1 Classification of fuzzy neural networks; S1.2 Fuzzy neural networks with fuzzy operators; S1.3 Puzzified neural networks; 1.3.1 Learning algorithm for regular FNN's; 1.3.2 Universal approximation of regular FNN's; S1.4 Fuzzy systems and fuzzy inference networks; 1.4.1 Fuzzy systems; 1.4.2 Fuzzy inference networks; S1.5 Fuzzy techniques in image restoration; 1.5.1 Crisp nonlinear filters; 1.5.2 Fuzzy filters; S1.6 Notations and preliminaries; S1.7 Outline of the topics of the chapters; References. | |
| 505 | 8 | |a Chapter II Fuzzy Neural Networks for Storing and ClassifyingS2.1 Two layer max-min fuzzy associative memory; 2.1.1 FAM with threshold; 2.1.2 Simulation example; S2.2 Fuzzy 6-learning algorithm; 2.2.1 FAM's based on 'V -- /\'; 2.2.2 FAM's based on 'V -- *'; S2.3 BP learning algorithm of FAM's; 2.3.1 Two analytic functions; 2.3.2 BP learning algorithm; S2.4 Fuzzy ART and fuzzy ARTMAP; 2.4.1 ART1 architecture; 2.4.2 Fuzzy ART; 2.4.3 Fuzzy ARTMAP; 2.4.4 Real examples; References; Chapter III Fuzzy Associative Memory-Feedback Networks; S3.1 Fuzzy Hopfield networks. | |
| 505 | 8 | |a 3.1.1 Attractor and attractive basin3.1.2 Learning algorithm based on fault-tolerance; 3.1.3 Simulation example; S3.2 Fuzzy Hopfield network with threshold; 3.2.1 Attractor and stability; 3.2.2 Analysis of fault-tolerance; S3.3 Stability and fault-tolerance of FBAM; 3.3.1 Stability analysis; 3.3.2 Fault-tolerance analysis; 3.3.3 A simulation example; S3.4 Learning algorithm for FBAM; 3.4.1 Learning algorithm based on fault-tolerance; 3.4.2 A simulation example; 3.4.3 Optimal fault-tolerance; 3.4.4 An example; S3.5 Connection network of FBAM; 3.5.1 Fuzzy row-restricted matrix. | |
| 505 | 8 | |a 3.5.2 The connection relations of attractors3.5.3 The elementary memory of (R L); 3.5.4 The transition laws of states; S3.6 Equilibrium analysis of fuzzy Hopfield network; 3.6.1 Connection relations of attractors; 3.6.2 Elementary memory of W; 3.6.3 The state transition laws; References; Chapter IV Regular Fuzzy Neural Networks; S4.1 Regular fuzzy neuron and regular FNN; 4.1.1 Regular fuzzy neuron; 4.1.2 Regular fuzzy neural network; 4.1.3 A counter example of universal approximation; 4.1.4 An example of universal approximation; S4.2 Learning algorithms; 4.2.1 Preliminaries. | |
| 505 | 8 | |a 4.2.2 Calculus of V -- /\ functions4.2.3 Error function; 4.2.4 Partial derivatives of error function; 4.2.5 Learning algorithm and simulation; S4.3 Conjugate gradient algorithm for fuzzy weights; 4.3.1 Fuzzy CG algorithm and convergence; 4.3.2 GA for finding optimal learning constant; 4.3.3 Simulation examples; S4.4 Universal approximation to fuzzy valued functions; 4.4.1 Fuzzy valued Bernstein polynomial; 4.4.2 Four-layer regular feedforward FNN; 4.4.3 An example; S4.5 Approximation analysis of regular FNN; 4.5.1 Closure fuzzy mapping; 4.5.2 Learning algorithm. | |
| 520 | |a This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. Special emphasis is placed on the fundamental concepts and architecture analysis of fuzzy neural networks. The book is unique in treating all kinds of fuzzy neural networks and their learning algorithms and universal approximations, and employing simulation examples which are carefully designed to he. | ||
| 650 | 0 | |a Neural networks (Computer science) |0 http://id.loc.gov/authorities/subjects/sh90001937 | |
| 650 | 0 | |a Fuzzy systems. |0 http://id.loc.gov/authorities/subjects/sh85052628 | |
| 650 | 2 | |a Neural Networks, Computer |0 https://id.nlm.nih.gov/mesh/D016571 | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
| 650 | 6 | |a Systèmes flous. | |
| 650 | 7 | |a COMPUTERS |x Neural Networks. |2 bisacsh | |
| 650 | 7 | |a Fuzzy systems |2 fast | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast | |
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Datensatz im Suchindex
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| author | Liu, Puyin |
| author2 | Li, Hong-Xing, 1953- |
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| callnumber-raw | QA76.87 .L58 2004eb |
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| contents | Foreword; Preface; Chapter I Introduction; S1.1 Classification of fuzzy neural networks; S1.2 Fuzzy neural networks with fuzzy operators; S1.3 Puzzified neural networks; 1.3.1 Learning algorithm for regular FNN's; 1.3.2 Universal approximation of regular FNN's; S1.4 Fuzzy systems and fuzzy inference networks; 1.4.1 Fuzzy systems; 1.4.2 Fuzzy inference networks; S1.5 Fuzzy techniques in image restoration; 1.5.1 Crisp nonlinear filters; 1.5.2 Fuzzy filters; S1.6 Notations and preliminaries; S1.7 Outline of the topics of the chapters; References. Chapter II Fuzzy Neural Networks for Storing and ClassifyingS2.1 Two layer max-min fuzzy associative memory; 2.1.1 FAM with threshold; 2.1.2 Simulation example; S2.2 Fuzzy 6-learning algorithm; 2.2.1 FAM's based on 'V -- /\'; 2.2.2 FAM's based on 'V -- *'; S2.3 BP learning algorithm of FAM's; 2.3.1 Two analytic functions; 2.3.2 BP learning algorithm; S2.4 Fuzzy ART and fuzzy ARTMAP; 2.4.1 ART1 architecture; 2.4.2 Fuzzy ART; 2.4.3 Fuzzy ARTMAP; 2.4.4 Real examples; References; Chapter III Fuzzy Associative Memory-Feedback Networks; S3.1 Fuzzy Hopfield networks. 3.1.1 Attractor and attractive basin3.1.2 Learning algorithm based on fault-tolerance; 3.1.3 Simulation example; S3.2 Fuzzy Hopfield network with threshold; 3.2.1 Attractor and stability; 3.2.2 Analysis of fault-tolerance; S3.3 Stability and fault-tolerance of FBAM; 3.3.1 Stability analysis; 3.3.2 Fault-tolerance analysis; 3.3.3 A simulation example; S3.4 Learning algorithm for FBAM; 3.4.1 Learning algorithm based on fault-tolerance; 3.4.2 A simulation example; 3.4.3 Optimal fault-tolerance; 3.4.4 An example; S3.5 Connection network of FBAM; 3.5.1 Fuzzy row-restricted matrix. 3.5.2 The connection relations of attractors3.5.3 The elementary memory of (R L); 3.5.4 The transition laws of states; S3.6 Equilibrium analysis of fuzzy Hopfield network; 3.6.1 Connection relations of attractors; 3.6.2 Elementary memory of W; 3.6.3 The state transition laws; References; Chapter IV Regular Fuzzy Neural Networks; S4.1 Regular fuzzy neuron and regular FNN; 4.1.1 Regular fuzzy neuron; 4.1.2 Regular fuzzy neural network; 4.1.3 A counter example of universal approximation; 4.1.4 An example of universal approximation; S4.2 Learning algorithms; 4.2.1 Preliminaries. 4.2.2 Calculus of V -- /\ functions4.2.3 Error function; 4.2.4 Partial derivatives of error function; 4.2.5 Learning algorithm and simulation; S4.3 Conjugate gradient algorithm for fuzzy weights; 4.3.1 Fuzzy CG algorithm and convergence; 4.3.2 GA for finding optimal learning constant; 4.3.3 Simulation examples; S4.4 Universal approximation to fuzzy valued functions; 4.4.1 Fuzzy valued Bernstein polynomial; 4.4.2 Four-layer regular feedforward FNN; 4.4.3 An example; S4.5 Approximation analysis of regular FNN; 4.5.1 Closure fuzzy mapping; 4.5.2 Learning algorithm. |
| ctrlnum | (OCoLC)262616298 |
| dewey-full | 006.32 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
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| discipline | Informatik |
| format | Electronic eBook |
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code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="347" ind1=" " ind2=" "><subfield code="a">data file</subfield></datafield><datafield tag="490" ind1="1" ind2=" "><subfield code="a">Series in machine perception and artificial intelligence ;</subfield><subfield code="v">v. 59</subfield></datafield><datafield tag="504" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references and index.</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Print version record.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Foreword; Preface; Chapter I Introduction; S1.1 Classification of fuzzy neural networks; S1.2 Fuzzy neural networks with fuzzy operators; S1.3 Puzzified neural networks; 1.3.1 Learning algorithm for regular FNN's; 1.3.2 Universal approximation of regular FNN's; S1.4 Fuzzy systems and fuzzy inference networks; 1.4.1 Fuzzy systems; 1.4.2 Fuzzy inference networks; S1.5 Fuzzy techniques in image restoration; 1.5.1 Crisp nonlinear filters; 1.5.2 Fuzzy filters; S1.6 Notations and preliminaries; S1.7 Outline of the topics of the chapters; References.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Chapter II Fuzzy Neural Networks for Storing and ClassifyingS2.1 Two layer max-min fuzzy associative memory; 2.1.1 FAM with threshold; 2.1.2 Simulation example; S2.2 Fuzzy 6-learning algorithm; 2.2.1 FAM's based on 'V -- /\'; 2.2.2 FAM's based on 'V -- *'; S2.3 BP learning algorithm of FAM's; 2.3.1 Two analytic functions; 2.3.2 BP learning algorithm; S2.4 Fuzzy ART and fuzzy ARTMAP; 2.4.1 ART1 architecture; 2.4.2 Fuzzy ART; 2.4.3 Fuzzy ARTMAP; 2.4.4 Real examples; References; Chapter III Fuzzy Associative Memory-Feedback Networks; S3.1 Fuzzy Hopfield networks.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.1.1 Attractor and attractive basin3.1.2 Learning algorithm based on fault-tolerance; 3.1.3 Simulation example; S3.2 Fuzzy Hopfield network with threshold; 3.2.1 Attractor and stability; 3.2.2 Analysis of fault-tolerance; S3.3 Stability and fault-tolerance of FBAM; 3.3.1 Stability analysis; 3.3.2 Fault-tolerance analysis; 3.3.3 A simulation example; S3.4 Learning algorithm for FBAM; 3.4.1 Learning algorithm based on fault-tolerance; 3.4.2 A simulation example; 3.4.3 Optimal fault-tolerance; 3.4.4 An example; S3.5 Connection network of FBAM; 3.5.1 Fuzzy row-restricted matrix.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.5.2 The connection relations of attractors3.5.3 The elementary memory of (R L); 3.5.4 The transition laws of states; S3.6 Equilibrium analysis of fuzzy Hopfield network; 3.6.1 Connection relations of attractors; 3.6.2 Elementary memory of W; 3.6.3 The state transition laws; References; Chapter IV Regular Fuzzy Neural Networks; S4.1 Regular fuzzy neuron and regular FNN; 4.1.1 Regular fuzzy neuron; 4.1.2 Regular fuzzy neural network; 4.1.3 A counter example of universal approximation; 4.1.4 An example of universal approximation; S4.2 Learning algorithms; 4.2.1 Preliminaries.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4.2.2 Calculus of V -- /\ functions4.2.3 Error function; 4.2.4 Partial derivatives of error function; 4.2.5 Learning algorithm and simulation; S4.3 Conjugate gradient algorithm for fuzzy weights; 4.3.1 Fuzzy CG algorithm and convergence; 4.3.2 GA for finding optimal learning constant; 4.3.3 Simulation examples; S4.4 Universal approximation to fuzzy valued functions; 4.4.1 Fuzzy valued Bernstein polynomial; 4.4.2 Four-layer regular feedforward FNN; 4.4.3 An example; S4.5 Approximation analysis of regular FNN; 4.5.1 Closure fuzzy mapping; 4.5.2 Learning algorithm.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. Special emphasis is placed on the fundamental concepts and architecture analysis of fuzzy neural networks. 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| id | ZDB-4-EBA-ocn262616298 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:16:33Z |
| institution | BVB |
| isbn | 9789812794215 9812794212 |
| language | English |
| oclc_num | 262616298 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xvii, 376 pages) |
| psigel | ZDB-4-EBA |
| publishDate | 2004 |
| publishDateSearch | 2004 |
| publishDateSort | 2004 |
| publisher | World Scientific, |
| record_format | marc |
| series | Series in machine perception and artificial intelligence ; |
| series2 | Series in machine perception and artificial intelligence ; |
| spelling | Liu, Puyin. http://id.loc.gov/authorities/names/nb2004305717 Fuzzy neural network theory and application / Puyin Liu, Hongxing Li. River Edge, NJ : World Scientific, 2004. 1 online resource (xvii, 376 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier data file Series in machine perception and artificial intelligence ; v. 59 Includes bibliographical references and index. Print version record. Foreword; Preface; Chapter I Introduction; S1.1 Classification of fuzzy neural networks; S1.2 Fuzzy neural networks with fuzzy operators; S1.3 Puzzified neural networks; 1.3.1 Learning algorithm for regular FNN's; 1.3.2 Universal approximation of regular FNN's; S1.4 Fuzzy systems and fuzzy inference networks; 1.4.1 Fuzzy systems; 1.4.2 Fuzzy inference networks; S1.5 Fuzzy techniques in image restoration; 1.5.1 Crisp nonlinear filters; 1.5.2 Fuzzy filters; S1.6 Notations and preliminaries; S1.7 Outline of the topics of the chapters; References. Chapter II Fuzzy Neural Networks for Storing and ClassifyingS2.1 Two layer max-min fuzzy associative memory; 2.1.1 FAM with threshold; 2.1.2 Simulation example; S2.2 Fuzzy 6-learning algorithm; 2.2.1 FAM's based on 'V -- /\'; 2.2.2 FAM's based on 'V -- *'; S2.3 BP learning algorithm of FAM's; 2.3.1 Two analytic functions; 2.3.2 BP learning algorithm; S2.4 Fuzzy ART and fuzzy ARTMAP; 2.4.1 ART1 architecture; 2.4.2 Fuzzy ART; 2.4.3 Fuzzy ARTMAP; 2.4.4 Real examples; References; Chapter III Fuzzy Associative Memory-Feedback Networks; S3.1 Fuzzy Hopfield networks. 3.1.1 Attractor and attractive basin3.1.2 Learning algorithm based on fault-tolerance; 3.1.3 Simulation example; S3.2 Fuzzy Hopfield network with threshold; 3.2.1 Attractor and stability; 3.2.2 Analysis of fault-tolerance; S3.3 Stability and fault-tolerance of FBAM; 3.3.1 Stability analysis; 3.3.2 Fault-tolerance analysis; 3.3.3 A simulation example; S3.4 Learning algorithm for FBAM; 3.4.1 Learning algorithm based on fault-tolerance; 3.4.2 A simulation example; 3.4.3 Optimal fault-tolerance; 3.4.4 An example; S3.5 Connection network of FBAM; 3.5.1 Fuzzy row-restricted matrix. 3.5.2 The connection relations of attractors3.5.3 The elementary memory of (R L); 3.5.4 The transition laws of states; S3.6 Equilibrium analysis of fuzzy Hopfield network; 3.6.1 Connection relations of attractors; 3.6.2 Elementary memory of W; 3.6.3 The state transition laws; References; Chapter IV Regular Fuzzy Neural Networks; S4.1 Regular fuzzy neuron and regular FNN; 4.1.1 Regular fuzzy neuron; 4.1.2 Regular fuzzy neural network; 4.1.3 A counter example of universal approximation; 4.1.4 An example of universal approximation; S4.2 Learning algorithms; 4.2.1 Preliminaries. 4.2.2 Calculus of V -- /\ functions4.2.3 Error function; 4.2.4 Partial derivatives of error function; 4.2.5 Learning algorithm and simulation; S4.3 Conjugate gradient algorithm for fuzzy weights; 4.3.1 Fuzzy CG algorithm and convergence; 4.3.2 GA for finding optimal learning constant; 4.3.3 Simulation examples; S4.4 Universal approximation to fuzzy valued functions; 4.4.1 Fuzzy valued Bernstein polynomial; 4.4.2 Four-layer regular feedforward FNN; 4.4.3 An example; S4.5 Approximation analysis of regular FNN; 4.5.1 Closure fuzzy mapping; 4.5.2 Learning algorithm. This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. Special emphasis is placed on the fundamental concepts and architecture analysis of fuzzy neural networks. The book is unique in treating all kinds of fuzzy neural networks and their learning algorithms and universal approximations, and employing simulation examples which are carefully designed to he. Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Fuzzy systems. http://id.loc.gov/authorities/subjects/sh85052628 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Réseaux neuronaux (Informatique) Systèmes flous. COMPUTERS Neural Networks. bisacsh Fuzzy systems fast Neural networks (Computer science) fast Li, Hong-Xing, 1953- https://id.oclc.org/worldcat/entity/E39PCjDVykq7J4xj9q3kx9HqYq http://id.loc.gov/authorities/names/n95006038 Print version: Liu, Puyin. Fuzzy neural network theory and application. River Edge, NJ : World Scientific, 2004 9812387862 9789812387868 (DLC) 2007296333 (OCoLC)56430192 Series in machine perception and artificial intelligence ; v. 59. http://id.loc.gov/authorities/names/n91107585 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=235586 Volltext |
| spellingShingle | Liu, Puyin Fuzzy neural network theory and application / Series in machine perception and artificial intelligence ; Foreword; Preface; Chapter I Introduction; S1.1 Classification of fuzzy neural networks; S1.2 Fuzzy neural networks with fuzzy operators; S1.3 Puzzified neural networks; 1.3.1 Learning algorithm for regular FNN's; 1.3.2 Universal approximation of regular FNN's; S1.4 Fuzzy systems and fuzzy inference networks; 1.4.1 Fuzzy systems; 1.4.2 Fuzzy inference networks; S1.5 Fuzzy techniques in image restoration; 1.5.1 Crisp nonlinear filters; 1.5.2 Fuzzy filters; S1.6 Notations and preliminaries; S1.7 Outline of the topics of the chapters; References. Chapter II Fuzzy Neural Networks for Storing and ClassifyingS2.1 Two layer max-min fuzzy associative memory; 2.1.1 FAM with threshold; 2.1.2 Simulation example; S2.2 Fuzzy 6-learning algorithm; 2.2.1 FAM's based on 'V -- /\'; 2.2.2 FAM's based on 'V -- *'; S2.3 BP learning algorithm of FAM's; 2.3.1 Two analytic functions; 2.3.2 BP learning algorithm; S2.4 Fuzzy ART and fuzzy ARTMAP; 2.4.1 ART1 architecture; 2.4.2 Fuzzy ART; 2.4.3 Fuzzy ARTMAP; 2.4.4 Real examples; References; Chapter III Fuzzy Associative Memory-Feedback Networks; S3.1 Fuzzy Hopfield networks. 3.1.1 Attractor and attractive basin3.1.2 Learning algorithm based on fault-tolerance; 3.1.3 Simulation example; S3.2 Fuzzy Hopfield network with threshold; 3.2.1 Attractor and stability; 3.2.2 Analysis of fault-tolerance; S3.3 Stability and fault-tolerance of FBAM; 3.3.1 Stability analysis; 3.3.2 Fault-tolerance analysis; 3.3.3 A simulation example; S3.4 Learning algorithm for FBAM; 3.4.1 Learning algorithm based on fault-tolerance; 3.4.2 A simulation example; 3.4.3 Optimal fault-tolerance; 3.4.4 An example; S3.5 Connection network of FBAM; 3.5.1 Fuzzy row-restricted matrix. 3.5.2 The connection relations of attractors3.5.3 The elementary memory of (R L); 3.5.4 The transition laws of states; S3.6 Equilibrium analysis of fuzzy Hopfield network; 3.6.1 Connection relations of attractors; 3.6.2 Elementary memory of W; 3.6.3 The state transition laws; References; Chapter IV Regular Fuzzy Neural Networks; S4.1 Regular fuzzy neuron and regular FNN; 4.1.1 Regular fuzzy neuron; 4.1.2 Regular fuzzy neural network; 4.1.3 A counter example of universal approximation; 4.1.4 An example of universal approximation; S4.2 Learning algorithms; 4.2.1 Preliminaries. 4.2.2 Calculus of V -- /\ functions4.2.3 Error function; 4.2.4 Partial derivatives of error function; 4.2.5 Learning algorithm and simulation; S4.3 Conjugate gradient algorithm for fuzzy weights; 4.3.1 Fuzzy CG algorithm and convergence; 4.3.2 GA for finding optimal learning constant; 4.3.3 Simulation examples; S4.4 Universal approximation to fuzzy valued functions; 4.4.1 Fuzzy valued Bernstein polynomial; 4.4.2 Four-layer regular feedforward FNN; 4.4.3 An example; S4.5 Approximation analysis of regular FNN; 4.5.1 Closure fuzzy mapping; 4.5.2 Learning algorithm. Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Fuzzy systems. http://id.loc.gov/authorities/subjects/sh85052628 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Réseaux neuronaux (Informatique) Systèmes flous. COMPUTERS Neural Networks. bisacsh Fuzzy systems fast Neural networks (Computer science) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh90001937 http://id.loc.gov/authorities/subjects/sh85052628 https://id.nlm.nih.gov/mesh/D016571 |
| title | Fuzzy neural network theory and application / |
| title_auth | Fuzzy neural network theory and application / |
| title_exact_search | Fuzzy neural network theory and application / |
| title_full | Fuzzy neural network theory and application / Puyin Liu, Hongxing Li. |
| title_fullStr | Fuzzy neural network theory and application / Puyin Liu, Hongxing Li. |
| title_full_unstemmed | Fuzzy neural network theory and application / Puyin Liu, Hongxing Li. |
| title_short | Fuzzy neural network theory and application / |
| title_sort | fuzzy neural network theory and application |
| topic | Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Fuzzy systems. http://id.loc.gov/authorities/subjects/sh85052628 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Réseaux neuronaux (Informatique) Systèmes flous. COMPUTERS Neural Networks. bisacsh Fuzzy systems fast Neural networks (Computer science) fast |
| topic_facet | Neural networks (Computer science) Fuzzy systems. Neural Networks, Computer Réseaux neuronaux (Informatique) Systèmes flous. COMPUTERS Neural Networks. Fuzzy systems |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=235586 |
| work_keys_str_mv | AT liupuyin fuzzyneuralnetworktheoryandapplication AT lihongxing fuzzyneuralnetworktheoryandapplication |