Recent Advances in Intelligent Paradigms and Applications / Edition 1

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More About This Textbook


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: 9783790815382
  • Publisher: Physica-Verlag HD
  • Publication date: 4/10/2003
  • Series: Studies in Fuzziness and Soft Computing Series, #113
  • Edition description: 2003
  • Edition number: 1
  • Pages: 272
  • Product dimensions: 9.21 (w) x 6.14 (h) x 0.69 (d)

Table of Contents

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