Fuzzy Logic and Neural Network Handbook

Overview

A centerpiece of the efforts to develop intelligent computer systems is the use of logic states between 0 and 1-fuzzy logic-and the neural networks with which they are closely linked. Here is the first comprehensive,practical treatment of this high-interest topic. For the first time in a single source,readers have access to algorithms,design guidelines,and examples of current applications-from signal processing and power systems to home appliance design and manufacturing. Featuring contributions from the top ...
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Overview

A centerpiece of the efforts to develop intelligent computer systems is the use of logic states between 0 and 1-fuzzy logic-and the neural networks with which they are closely linked. Here is the first comprehensive,practical treatment of this high-interest topic. For the first time in a single source,readers have access to algorithms,design guidelines,and examples of current applications-from signal processing and power systems to home appliance design and manufacturing. Featuring contributions from the top names in the field,this authoritative handbook is the essential reference on fuzzy logic-from its conceptual underpinnings,to nitty-gritty systems design issues,to its most exciting new breakthroughs.

Fuzzy logic and neural networks, which are linked, have emerged as the keystone of developing intelligent computer systems with broad application throughout engineering and science. Neural nets allow them to better manage uncertainty. That combination gives the ability to solve many classes of problems more efficiently. This book deals with principles and algorithms, applications and architectures and systems.

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Editorial Reviews

Booknews
A practical reference that presents concise and comprehensive reports on the major activities in fuzzy logic and neural networks, with emphasis on the applications and systems of interest to computer engineers. Each of the 31 chapters focuses on the most important activity of a specific topic, and the chapters are organized into three parts: principles and algorithms; applications; and architectures and systems. The applications for fuzzy logic include home appliance design and manufacturing process; those for neural networks include radar, sonar, and speech signal processing, remote sensing, and electrical power systems. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780070111899
  • Publisher: McGraw-Hill Companies, The
  • Publication date: 12/1/1995
  • Series: Computer Systems Ser.
  • Pages: 1
  • Product dimensions: 6.26 (w) x 9.27 (h) x 2.09 (d)

Table of Contents

Contributors
Preface
Ch. 1 Learning, Categorization, Rule Formation, and Prediction by Fuzzy Neural Networks
Ch. 2 A Review of Probabilistic, Fuzzy, and Neural Models for Pattern Recognition
Ch. 3 Probabilistic Neural Networks and General Regression Neural Networks
Ch. 4 Recurrent Neural Networks for Optimization
Ch. 5 The Semantics of Approximate Reasoning
Ch. 6 Constrained Defuzzification in Data-Driven Adaptive Fuzzy Logic Controllers
Ch. 7 Efficient Methods for Fuzzy Rule Extraction from Numerical Data
Ch. 8 Fuzzy Relational Equation Problem Solving: Tools for Modeling
Ch. 9 Additive Fuzzy Systems: From Function Approximation to Learning
Ch. 10 Neural Net Process Monitoring and Optimal Control
Ch. 11 Neural Networks for Database Applications
Ch. 12 A Mutual Information-Based learning Strategy and Its Application to Radar
Ch. 13 Sonar Signal Processing and Classification Using Neural Networks
Ch. 14 Neural Networks for Speech Recognition
Ch. 15 Neural Networks for Classification of Remotely Sensed Images
Ch. 16 Neural Networks for Handwritten Character Recognition
Ch. 17 Neural Networks for Broadcast Scheduling in Telecommunications
Ch. 18 Applications of Neural Networks in Semiconductor Manufacturing Processes
Ch. 19 Modeling Quarterly Returns on the FTSE: A Comparative Study with Regression and Neural Networks
Ch. 20 Application of Neural Networks and Fuzzy Logic in Nondestructive Evaluation of Materials
Ch. 21 Fuzzy Logic for Home Appliances
Ch. 22 A Fuzzy Logic Controller to Drill Small Holes
Ch. 23 Hardware Fuzzy Inference Systems - Architecture, Design, and Implementation
Ch. 24 Reactive Control Using Fuzzy Logic
Ch. 25 A Hybrid Fuzzy Expert System Shell for Automated Medical Diagnosis
Ch. 26 Cerebellar Model Arithmetic Computer
Ch. 27 Learning in Neural Networks: VLSI Implementation Strategies
Ch. 28 Morphological Networks
Ch. 29 Chaotic Neural Network Architecture
Ch. 30 Artificial Neural Systems Based on Free-Space Optical Interconnects
Ch. 31 A General-Purpose Analog Neural Computer for Real-Time Spatiotemporal Pattern Analysis: Visual Motion Estimation
Index
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