By Jun Wang, Andrew Kusiak
Regardless of the big quantity of courses dedicated to neural networks, fuzzy common sense, 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 latest advances during this zone and cutting-edge functions. This finished instruction manual comprises a superb stability of tutorials and new effects, that enables you to
Manufacturing purposes play a number one position in growth, and this guide grants a prepared connection with advisor you simply via those advancements.
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Extra resources for Computational Intelligence in Manufacturing Handbook (The Mechanical Engineering Handbook Series)
Based on the ART-1 neural network, Kusiak and Chung  developed a neural network model called GT/ART for solving GT problems by block diagonalizing machine-part incidence matrices. This work showed that the GT/ART neural network is more suitable for grouping machine cells and part families than other nonlearning algorithms and other neural networks such as multilayer neural networks with the BP learning algorithm. The GT/ART model allows learning new patterns and keeping existing weights stable (plasticity vs.
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Computational Intelligence in Manufacturing Handbook (The Mechanical Engineering Handbook Series) by Jun Wang, Andrew Kusiak