Practical Applications of Computational Intelligence Techniques / Edition 1

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Computational intelligence paradigms have attracted the growing interest of researchers, scientists, engineers and application engineers in a number of everyday applications. These applications are not limited to any particular field and include engineering, business, banking and consumer electronics. Computational intelligence paradigms include artificial intelligence, artificial neural networks, fuzzy systems and evolutionary computing. Artificial neural networks can mimic the biological information processing mechanism in a very limited sense. Evolutionary computing algorithms are used for optimisation applications, and fuzzy logic provides a basis for representing uncertain and imprecise knowledge.
Practical Applications of Computational Intelligence Techniques contains twelve chapters providing actual application of these techniques in the real world. Such examples include, but are not limited to, intelligent household appliances, aerial spray models, industrial applications and medical diagnostics and practice. This book will be useful to researchers, practicing engineers/scientists and students, who are interested in developing practical applications in a computational intelligence environment.

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Product Details

  • ISBN-13: 9780792373209
  • Publisher: Springer Netherlands
  • Publication date: 4/30/2001
  • Series: International Series in Intelligent Technologies , #16
  • Edition description: 2001
  • Edition number: 1
  • Pages: 400
  • Product dimensions: 9.21 (w) x 6.14 (h) x 0.94 (d)

Table of Contents

1. An introduction to computational intelligence paradigms; A. Konar, L.C. Jain. 2. Networked virtual park; N. Magnenat-Thalmann, et al. 3. Commercial coin recognisers using neural and fuzzy techniques; J.M. Moreno, et al. 4. Fuzzy techniques in intelligent household appliances; M. Mraz, et al. 5. Neural prediction in industry: increasing reliability through use of confidence measures and model combination; P.J. Edwards, et al. 6. Handling the back calculation problem in aerial spray models using a genetic algorithm; W.D. Potter, et al. 7. Genetic algorithm optimization of a filament winding process modeled in WITNESS; E. Wilson, et al. 8. Genetic algorithm for optimizing the gust loads for predicting aircraft loads and dynamic response; R. Mehrotra, et al. 9. A shastic dynamic programming technique for property market timing; T.C. Chin, G.T. Mills. 10. A hybrid approach to breast cancer diagnosis; M. Sordo, et al. 11. Artificial neural network as a computer aid for lung disease detection and classification in ventilation-perfusion lung scans; G.D. Tourassi, et al. 12. Neural network for classification of focal liver lesions in ultrasound images; H. Yoshida. Index.

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