Feasibility and robustness of transiently chaotic neural networks applied to the cell formation problem
研究了瞬态混沌神经网络在单元形成问题中的动态特性，分析了其求解的可行性与鲁棒性，并讨论了参数初始值的设置方法，对制造系统设计者选择算法有参考价值。
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 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.