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Recent Advances in Intelligent Paradigms and Applications / Edition 1

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More About This Textbook

Overview

The last few decades have seen a new era of artificial intelligence focusing on emulating humans, either in their behaviour or in their neurophysiology. Computational intelligence is a well-established paradigm, where new theories with a sound biological understanding have been evolving. This volume is a rare collection of 12 chapters compiling the latest state-of-the-art research in the area of intelligent paradigms authored by the world leading well-established experts in the field. Each chapter focuses on different aspects of intelligent systems. The chapters present the latest theoretical developments as well as practical applications of these latest technologies.

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Product Details

  • ISBN-13: 9783790825213
  • Publisher: Physica-Verlag HD
  • Publication date: 12/15/2010
  • Series: Studies in Fuzziness and Soft Computing Series , #113
  • Edition description: Softcover reprint of hardcover 1st ed. 2003
  • Edition number: 1
  • Pages: 272
  • Product dimensions: 9.21 (w) x 6.14 (h) x 0.61 (d)

Table of Contents

1. Intelligent systems: architectures and perspectives.- 2. Hybrid architecture for autonomous robots, based on representations, perception and intelligent control.- 3. An intuitionistic fuzzy set based approach to intelligent data analysis: an application to medical diagnosis.- 4. A fuzzy inference methodology based on the fuzzification of set inclusion.- 5. A fuzzy approach to job-shop scheduling problem based on imprecise processing times.- 6. On efficient representation of expert knowledge by fuzzy logic.- 7. Discovering efficient learning rules for feedforward neural networks using genetic programming.- 8. Neuro-fuzzy methods for modeling and identification.- 9. Constrained two dimensional bin packing using a genetic algorithm.- 10. Sequential and distributed evolutionary algorithms for combinatorial optimization problems.- 11. Embodied emotional agent in intelligent training system.- 12. Optimizing intelligent agent’s constraint satisfaction with neural networks.

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