Description |
xii, 334 pages : illustrations ; 24 cm |
Series |
Studies in fuzziness and soft computing ; vol. 36 |
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Studies in fuzziness and soft computing ; vol. 36
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Contents |
Ch. 1. An Introduction to Innovative Teaching and Learning / D. Tedman and L. C. Jain -- Ch. 2. Teaching and Learning the AI Modeling / R. S. T. Lee and J. N. K. Liu -- Ch. 3. Artificial Intelligence Techniques for an Interdisciplinary Science Course / C. L. Karr, C. Sunal and C. Smith -- Ch. 4. On the Architecture of Intelligent Tutoring Systems and Its Application to a Neural Networks Course / J. F. Vega-Riveros -- Ch. 5. Teaching Knowledge Modeling at the Graduate Level - a Case Study / V. Devedzic -- Ch. 6. Innovative Modeling Techniques for Intelligent Tutoring Systems / V. Devedzic, D. Radovic and L. Jerinic -- Ch. 7. Teaching Course on Artificial Neural Networks / J. Fulcher -- Ch. 8. Innovative Education for Fuzzy Logic Stabilization of Electric Power Systems in a Matlab/Simulink Environment / T. Hiyama -- Ch. 9. A Neural Network Workbench for Teaching and Learning / W. L. Goh and S. K. Amarasinghe |
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Ch. 10. PRAM: A Courseware System for the Automatic Assessment of AI Programs / C. A. Higgins and F. Z. Mansouri |
Summary |
"Presented are innovative teaching and learning techniques for the teaching of knowledge-based paradigms. The main knowledge-based intelligent paradigms are expert systems, artificial neural networks, fuzzy systems and evolutionary computing. Experts systems are designed to mimic the performance of biological systems. Artificial neural networks can mimic the biological information processing mechanism in a very limited sense. Evolutionary computing algorithms are used for optimization applications, and fuzzy logic provides a basis for representing uncertain and imprecise knowledge."--BOOK JACKET |
Bibliography |
Includes bibliographical references and index |
Subject |
Expert systems (Computer science)
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Artificial intelligence.
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Computer-assisted instruction.
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LC no. |
99052137 |
ISBN |
3790812463 |
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