Hybrid Architectures for Intelligent Systems
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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NEURAL NETS AND FUZZY LOGIC
Basic Components of Modular Neural Networks
NODE ERROR ASSIGNMENT IN EXPERT NETWORKS
PERFORMANCE ISSUES OF A HYBRID SYMBOLIC
LEARNING SYSTEM FOR GRAMMARS AND LEXICONS
INTEGRATION OF NEURAL NETWORK TECHNIQUES WITH
A PARALLEL DISTRIBUTED APPROACH FOR KNOWLEDGE
Data Classification and Pattern Recognition
GoalDirected Training of Neural Networks
Application of Robotic Skill Acquisition to Aircraft
A HYBRID ARCHITECTURE FOR FUZZY
MODELS AND GUIDELINES FOR INTEGRATING EXPERT
FUZZY HYBRID SYSTEMS C Posey A Kandel
HYBRID DISTRIBUTEDLOCAL CONNECTIONIST
HIERARCHICAL STRUCTURES IN HYBRID SYSTEMS
RULE COMBINING A NEURAL NETWORK APPROACH
A PROBLEM SOLVING SYSTEM FOR DATA ANALYSIS
REPRESENTING EXPERT KNOWLEDGE IN NEURAL
AN INTELLIGENT HYBRID SYSTEM FOR WASTEWATER
A HYBRID NEURAL AND SYMBOLIC PROCESSING
AUTHORS BIOGRAPHICAL INFORMATION
Citi izdevumi - Skatīt visu
acquisition action activation airspeed algorithm allows analysis antecedent applications approach architecture artificial assigned associated cell classification clause combined complex components Computer concept conclusion connectionist connections contains corresponding decision defined described desired determine distributed domain Engineering error evaluated example execution existing expert system factors Figure final function fuzzy given goal grammar hidden hierarchical hybrid implemented important inference initial input integration intelligent International involves knowledge base knowledge-based layer learning Machine match methods modified neural network neurons node objects obtain operator optimal output param performance phase possible present problem problem solving procedure produce programming reasoning represent representation rule-based rules samples scheduling scheme Science selection shown solution specific strategies structure symbolic Table task techniques treatment unit University utilized variables weights