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Hybrid Architectures For Intelligent Systems / Edition 1

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Overview

Hybrid architecture for intelligent systems is a new field of artificial intelligence concerned with the development of the next generation of intelligent systems. This volume is the first book to delineate current research interests in hybrid architectures for intelligent systems.
The book is divided into two parts. The first part is devoted to the theory, methodologies, and algorithms of intelligent hybrid systems. The second part examines current applications of intelligent hybrid systems in areas such as data analysis, pattern classification and recognition, intelligent robot control, medical diagnosis, architecture, wastewater treatment, and flexible manufacturing systems.
Hybrid Architectures for Intelligent Systems is an important reference for computer scientists and electrical engineers involved with artificial intelligence, neural networks, parallel processing, robotics, and systems architecture.

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Editorial Reviews

Booknews
Reviews the current research in developing hybrid architectures for the next generation of intelligent computer systems. Reflects the field's focus on integrating the computational paradigms of ex pert systems and neural networks, both conventional and fuzzy, and exploring the similarities between the underlying structures. Two-thirds of the text deals with the theory, methodologies, and algorithms of intelligent hybrid systems; the rest reports some applications in robots, medical diagnosis, architecture, and other areas. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780849342295
  • Publisher: CRC Press
  • Publication date: 2/21/1992
  • Edition number: 1
  • Pages: 448
  • Product dimensions: 7.00 (w) x 10.00 (h) x 1.00 (d)

Table of Contents

THEORY, METHODOLOGIES, ALGORITHMS. NEURAL NETS AND FUZZY LOGIC (A.F. da Rocha and R.R. Yager). Preliminary Concepts. Basic Components of Modular Neural Networks. Modular Networks. NODE ERROR ASSIGNMENT IN EXPERT NETWORKS (R.C. Lacher). Discrete Time Neural Computation. Node Error Assignment. Expert Network Backprop. Computational Experiments. LEARNING SYSTEM FOR GRAMMARS AND LEXICONS (J. D'souza and M. Schneider). Learning. Modifying the Lexicon. Modifying the Grammar. Creating a Grammar. Relationship between FEST and LSGL. INTEGRATION OF NEURAL NETWORK TECHNIQUES WITH APPROXIMATE REASONING IN KNOWLEDGE-BASED SYSTEMS (M.E. Cohen and D.L. Hudson). Approximate Reasoning in Knowledge-Based Systems. A Neural Network Learning Algorithm. Combined Techniques. A PARALLEL DISTRIBUTED APPROACH FOR KNOWLEDGE-BASED INFERENCE AND LEARNING (L-M Fu). A Knowledge-Based Neural Network. Inference. Learning. Evaluation. PERFORMANCE ISSUES OF A HYBRID SYMBOLIC, CONNECTIONIST ALGORITHM (L.O. Hall and S.G. Romaniuk). Network Structure. Learning. Analysis of Learning. Global Attribute Covering Algorithm. The Two Spirals Problem. Diagnosing Semiconductor Wafer Failures. A HYBRID ARCHITECTURE FOR FUZZY CONNECTIONIST EXPERT SYSTEM (R.J. Machado and A.F. da Rocha). Architecture of Fuzzy Connectionist Expert Systems. Example of an Application. MODELS AND GUIDELINES FOR INTEGRATING EXPERT SYSTEMS AND NEURAL NETWORKS (L.R. Medsker and D.L. Bailey). Models for ES/NN Synergy. Application Review. Guidelines for Developmental of Hybrid Systems. FUZZY HYBRID SYSTEMS (C. Posey, A. Kandel, and G. Langholz). Expert Systems. Neural Networks. Fuzzy Hybrid Systems. Conversion from Fuzzy Expert System to Neural Network. Knowledge Transfer from Neural Network to Expert System. Learning Results. HYBRID DISTRIBUTED/LOCAL CONNECTIONIST ARCHITECTURES (T. Samad). Distributed and Local Representations. Hybrid Distributed and Local Representations. Hybrid Distributed/Local Networks. A Connectionist Rule-Based System. A Knowledge Base Browser. Extensions. HIERARCHICAL STRUCTURES IN HYBRID SYSTEMS (M.F. Villa and K.D. Reilly). Example Hybrids and Hierarchies. Current Exploration. Future Work. RULE COMBINING-A NEURAL NETWORK APPROACH TO DESIGN OPTIMIZATION (Q. Yang). The Problem. Decision Making by Neural Computation. Knowledge Representation. Implementation Issues. APPLICATIONS. A PROBLEM SOLVING SYSTEM FOR DATA ANALYSIS, PATTERN CLASSIFICATION AND RECOGNITION (C.Y. Han and W.G. Wee). Review of Classification Methods. Data Classification and Pattern Recognition in Industrial Inspection Applications. Expert System Techniques. Knowledge Based Interactive Problem Solving Environment. ROBOTIC SKILL ACQUISITION BASED ON BIOLOGICAL PRINCIPLES (D.A. Handelman, S.H. Lane, and J.J. Gelfand). Goal-Directed Training of Neural Networks for Robotic Skill Acquisition. Application of Robotic Skill Acquisition to Aircraft Guidance and Control. MEDICAL DIAGNOSIS AND TREATMENT PLANS DERIVED FROM A HYBRID EXPERT SYSTEM (D.L. Hudson, M.E. Cohen, P.W. Banda, and M.S. Blois). Application. Model for Prognostic Factors. Chromatographic Analysis. REPRESENTING EXPERT KNOWLEDGE IN NEURAL NETS (R. Knaus). Why Use Expert Knowledge. The Scoring Problem. Need for a Hybrid System. Neural Net Encoding of Architectural Concepts. Knowledge Engineering from the Neural Perspective. Knowledge Constructs Expressed Neurally. Training. Related Work. AN INTELLIGENT HYBRID SYSTEM FOR WASTEWATER TREATMENT (S. Korvvidy and W.G. Wee). Design of WATTS. Analysis Phase of WATTS. Synthesis Phase of WATTS. Case Based Reasoning for Heuristic Search. Integration of Synthesis and CBR. A HYBRID NEURAL AND SYMBOLIC PROCESSING APPROACH TO FLEXIBLE MANUFACTURING SYSTEMS SCHEDULING (L.C. Rabelo). Hybrid Neural and Symbolic Processing Systems. Intelligent FMS Scheduling (IFMSS) Framework. Prototype Developed. Appendix A. Appendix B. AUTHORS' BIOGRAPHICAL INFORMATION. INDEX.

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