Neuro-fuzzy pattern recognition / editors, H. Bunke, A. Kandel.

Format:

Book

Contributor:

Bunke, H., editor.

Ḳendel, ʻA, editor.

Series:

Series in machine perception and artificial intelligence ; Volume 41.

Series in Machine Perception and Artificial Intelligence ; Volume 41

Language:

English

Subjects (All):

Pattern recognition systems.

Fuzzy systems.

Neural networks (Computer science).

Physical Description:

1 online resource (276 p.)

Place of Publication:

Singapore ; River Edge, New Jersey : World Scientific, 2000.

Language Note:

English

Summary:

Neural networks and fuzzy techniques are among the most promising approaches to pattern recognition. Neuro-fuzzy systems aim at combining the advantages of the two paradigms. This book is a collection of papers describing state-of-the-art work in this emerging field. It covers topics such as feature selection, classification, classifier training, and clustering. Also included are applications of neuro-fuzzy systems in speech recognition, land mine detection, medical image analysis, and autonomous vehicle control. The intended audience includes graduate students in computer science and related fields, as well as researchers at academic institutions and in industry.

Contents:

CONTENTS; Preface; METHODOLOGY; Simultaneous Feature Analysis and System Identification in a Neuro-Fuzzy Framework; 1 Introduction; 2 The Network Structure; 3 Learning of Feature Modulators and Rules; 4 Results; 4.1 Results on HANG; 4.2 Results on CHEM; 5 Conclusion; Bibliography; Neuro-fuzzy Model for Unsupervised Feature Extraction with Real Life Applications; 1 Introduction; 2 Feature Evaluation Index; 2.1 Definition; 2.2 Computation of membership function; 3 Feature Extraction; 4 Experimental Results; 4.1 Application to Iris data; 4.2 Application to mango-leaf data

4.3 Application to remote sensing images5 Conclusions; Bibliography; Appendix A; A.1 Operation of the Neural Network Model; A.2 Principal Component Analysis Network (PCAN) [9]; A Computational-Intelligence -Based Approach to Decision Support; 1. Introduction; 2. CI systems versus AI systems; 3. Main directions in synthesis of CI systems; 4. An implementation of a CI system for decision support -a neuro-fuzzy(-genetic) classifier; 4.1. Statement of the problem; 4.2. Neuro-fuzzy domain-knowledge representation scheme; 4.3. Neuro-fuzzy inference engine

5. A neuro-fuzzy-genetic decision support system for the glass identification problem in forensic science6. Conclusions; References; APPENDIX. ; Clustering Problem Using Fuzzy C-Means Algorithms and Unsupervised Neural Networks; 1 Introduction; 2 Fuzzy Clustering Techniques; 2.1 Fuzzy C-Means (FCM); 2.2 Penalized Fuzzy C-Means (PFCM); 2.3 Compensated Fuzzy C-Means (CFCM); 3 Fuzzy Competitive Learning Networks; 3.1 Conventional Competitive Learning Network; 3.2 Fuzzy Competitive Learning Networks; 4. Hopfield Network and Its modified Models; 4.1 Competitive Hopfield Neural Network (CHNN)

4.2 Fuzzy Hopfield Neural Network (FHNN)4.3 Penalized Fuzzy Hopfield Neural Network (PFHNN); 4.4 Compensated Fuzzy Hopfield Neural Network (CFHNN); 5 Chaotic Neural Networks (CNNs); 5.1 Chaotic Neural Network (CNN); 5.2 Annealed Chaotic Hopfield Neural Network (ACHNN); 6 Experimental Results; 7 Conclusions; References; Automatic Training of Min-Max Classifiers; 1. Introduction; 2. Overview of Min-Max Neural Networks; 3. ARC and PARC Training Algorithms; 4. Comparison Tests; 5. Generalized Min-Max Model; 6. Training GMM Networks; 7. Conclusions; References

Granular Computing in Pattern Recognition1. Introductory comments; 2. The determination of information granules; 3. Characteristics of information granules: size, variability and interaction; 3.1. A size of information granules; 3.2. Variability of information granules; 3.3. Expressing an interaction between information granules; 4. Data granulation and representation abilities; 5. An illustrative example; 6. Granular neural networks; 7. Conclusions; References; ART-Based Model Set for Pattern Recognition: FasArt Family; 1 Introduction; 2 Presentation of the FasArt familiy models

2.1 FasArt: the base neuro-fuzzy system

Notes:

Description based upon print version of record.

Includes bibliographical references at the end of each chapters.

Description based on print version record.

ISBN:

9789812792204

9812792201

OCLC:

827947368