By Jun Wang, Andrew Kusiak
Regardless of the big quantity of guides dedicated to neural networks, fuzzy good judgment, and evolutionary programming, few handle the functions of computational intelligence in layout and production. Computational Intelligence in production guide fills this void because it covers the latest advances during this zone and cutting-edge applications.This complete instruction manual includes an outstanding stability of tutorials and new effects, that permits you to:obtain present informationunderstand technical detailsassess learn potentials, anddefine destiny instructions of the sector production purposes play a number one position in growth, and this instruction manual can provide a prepared connection with consultant you simply via those advancements.
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Extra info for Computational Intelligence In Manufacturing Handbook (Handbook Series for Mechanical Engineering)
In addition to proper cell formation, the neural network also identifies bottleneck machines, which is especially useful in the case of very large part-machine incidence matrices where the visual identification of bottlenecks becomes intractable. It was also possible to determine the ratio in which bottleneck machines were shared among overlapping cells. The number of groups was arbitrarily chosen, which may not result in the best cellular manufacturing system. Lee et al.  presented an improved self-organizing neural network based on Kohonen’s unsupervised learning rule for part-family and machine-cell formation, bottleneck machine detection, and natural cluster generation.
Dagli and Huggahalli  pointed out the limitations of the basic ART-1 paradigm in cell formation and proposed a modification to make the performance more stable. The ART-1 paradigm was integrated with a decision support system that performed cost/performance analysis to arrive at an optimal solution. It was shown that with the original ART-1 paradigm the classification depends largely on order of presentation of the input vectors. Also, a deficient learning policy gradually causes a reduction in the responsibility of patterns, thus leading to a certain degree of inappropriate classification and a large number of groups than necessary.
Chen and Cheng  added two algorithms in the ART-1 neural network to alleviate the bottleneck machines and parts problem in machine-part cell formation. The first one was a rearrangement algorithm, which rearranged the machine groups in descending order according to the number of 1’s and their relative position in the machine-part incidence matrix. The second one was a reassignment algorithm, which reexamined the bottleneck machines and reassigned them to proper cells in order to reduce the number of exceptional elements.
Computational Intelligence In Manufacturing Handbook (Handbook Series for Mechanical Engineering) by Jun Wang, Andrew Kusiak