By Jun Wang, Andrew Kusiak

Regardless of the massive quantity of guides dedicated to neural networks, fuzzy good judgment, and evolutionary programming, few tackle the purposes of computational intelligence in layout and production. Computational Intelligence in production Handbook fills this void because it covers the newest advances during this zone and cutting-edge purposes. This finished instruction manual comprises a great stability of tutorials and new effects, that enables you to

  • receive present information
  • comprehend technical details
  • verify study potentials, and
  • outline destiny instructions of the field
  • Manufacturing functions play a number one function in growth, and this guide supplies a prepared connection with consultant you simply via those advancements.

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    Additional resources for Computational Intelligence in Manufacturing Handbook (The Mechanical Engineering Handbook Series)

    Sample text

    Systems Engineering, 6(1), 119-125. Pham D. T. , (1996), Intelligent Quality Systems, Springer-Verlag, London. Pham D. T. and Pham P. T. , (1988), Expert systems in mechanical and manufacturing engineering, Int. J. Adv. , special issue on knowledge based systems, 3(3), 3-21. Pham D. T. , (1993), A genetic algorithm based preliminary design system, Proc. IMechE, Part D: J Automobile Engineering, 207, 127-133. Price C. K. Quinlan J. , (1983), Learning efficient classification procedures and their applications to chess end games, in Machine Learning, An Artificial Intelligence Approach, Eds.

    1995), Intelligent operators and optimal genetic-based path planning for mobile robots, Proc. Int. Conf. Recent Advances in Mechatronics, Istanbul, Turkey, August, 1018-1023. Baker J. , (1985), Adaptive selection methods for genetic algorithms, Proc. First Int. Conf. Genetic Algorithms and Their Applications, Pittsburgh, PA, 101-111. Bas K. and Erkmen A. , (1995), Fuzzy preshape and reshape control of Anthrobot-III 5-fingered robot hand, Proc. Int. Conf. Recent Advances in Mechatronics, Istanbul, Turkey, August, 673-677.

    A high mutation rate introduces high diversity in the population and might cause instability. On the other hand, it is usually very difficult for a GA to find a global optimal solution with too low a mutation rate. 6 Fitness Evaluation Function The fitness evaluation unit in a GA acts as an interface between the GA and the optimisation problem. The GA assesses solutions for their quality according to the information produced by this unit and not by directly using information about their structure.

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