Foundations of neural networks, fuzzy systems, and knowledge engineering
著者
書誌事項
Foundations of neural networks, fuzzy systems, and knowledge engineering
MIT Press, c1996
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注記
"A Bradford book."
Bibliography: p. [523]-538
Includes index
内容説明・目次
内容説明
Neural networks and fuzzy systems are different aprpoaches to introducing human-like reasoning into expert systems. This text combines the study of these two subjects, their basics and their use, along with symbolic AI methods to build comprehensive artificial intelligence systems. Kasabov describes rule-based and connectionist techniques and then their combinations, with fuzzy logic included, showing the application of the different techniques to a set of simple prototype problems, which makes comparisons possible. A particular feature of the text is that it is filled with applications in engineering, business and finance. AI problems that cover most of the application-oriented research in the field (pattern recognition, speech and image processing, classification, planning, optimization, prediction, control, decision making, and game simulations) are discussed and illustrated with concrete examples.
Intended both as a text for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering, "Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering" has chapters structured for various levels of teaching and includes work by the author along with the classic material. Data sets for the examples in the book as well as an integrated software environment that can be used to solve the problems and do the exercises at the end of each chapter are available free through anonymous ftp.
目次
- Part 1 The faculty of knowledge engineering and problem solving: introduction to AI paradigms
- heuristic problem solving - genetic algorithms
- why expert systems, fuzzy systems, neural networks and hybrid systems for knowledge engineering and problem solving?
- generic and specific AI problems - pattern recognition and classification
- speech and language processing
- prediction
- planning, monitoring, diagnosis and control
- optimization, decision making and games playing
- a general approach to knowledge engineering
- problems and exercises. Part 2 Knowledge engineering and symbolic artificial intelligence: data, information and knowledge - major issues in knowledge engineering
- data analysis, data representation and data transformation
- information structures and knowledge representation
- methods for symbol manipulation and inference - inference as matching, inference as a search
- propositional logic
- predicate logic - PROLOG
- production systems
- expert systems
- uncertainties in knowledge-based systems - probabilistic methods
- nonprobabilistic methods for dealing with uncertainties
- machine-learning methods for knowledge engineering
- problems and exercises. Part 3 Neural networks for knowledge engineering and problem solving: neural networks as a problem-solving paradigm
- connectionist expert systems
- connectionist models for knowledge acquisition - one rule is worth a thousand data examples
- symbolic rules insertion in neural networks - connectionist production systems
- connectionist systems for pattern recognition and classification - image processing
- connectionist systems for speech processing
- connectionist systems for prediction
- connectionist systems for monitoring, control, diagnosis and planning
- connectionist systems for optimization and decision making
- connectionist systems for modelling strategic games
- problems. Part 4 Hybrid symbolic, fuzzy and connectionist systems - toward comprehensive artificial intelligence: the hybrid systems paradigm
- hybrid connectionist production systems
- hybrid connectionist logic programming systems
- hybrid fuzzy connectionist production systems
- ("pure") connectionist production systems - the NPS architecture (optional)
- hybrid systems for speech and language processing
- hybrid systems for decision making
- problems. Part 5 Neural networks, fuzzy systems and nonlinear dynamical systems chaos - toward new connectionist and fuzzy logic models: chaos
- fuzzy systems and chaos - new developments in fuzzy systems
- neural networks and chaos - new developments in neural networks.
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