Feasibility and robustness of transiently chaotic neural networks applied to the cell formation problem

Feasibility and robustness of transiently chaotic neural networks applied to the cell formation problem

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Article ID: iaor20043524
Country: United Kingdom
Volume: 42
Issue: 6
Start Page Number: 1065
End Page Number: 1082
Publication Date: Jan 2004
Journal: International Journal of Production Research
Authors: , ,
Keywords: neural networks
Abstract:

Cell formation is a key issue in the design of cellular manufacturing systems. Effective grouping of parts and machines can improve considerably the performance of the manufacturing cells. The transiently chaotic neural network (TCNN) is a recent methodology in intelligent computation that has the advantages of both the chaotic neural network and the Hopfield neural network. The present paper investigates the dynamics of the TCNN network and studies the feasibility and robustness of final solutions of TCNN when applied to the cell formation problem. The paper provides insight into the feasibility and robustness of TCNN for cell formation problems. It also discusses how to set the initial values of the TCNN parameters in the case of well-structured and ill-structured cell formation problems.

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