文章基本信息

标题：Desirability Improvement of Committee Machine to Solve Multiple Response Optimization Problems
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作者：Seyed Jafar Golestaneh ; Napsiah Ismail ; Mohd Khairol Anuar M. Ariffin 等
期刊名称：Advances in Artificial Neural Systems
印刷版ISSN：1687-7594
电子版ISSN：1687-7608
出版年度：2013
卷号：2013
DOI：10.1155/2013/628313
出版社：Hindawi Publishing Corporation
摘要：Multiple response optimization (MRO) problems are usually solved in three phases that include experiment design, modeling, and optimization. Committee machine (CM) as a set of some experts such as some artificial neural networks (ANNs) is used for modeling phase. Also, the optimization phase is done with different optimization techniques such as genetic algorithm (GA). The current paper is a development of recent authors' work on application of CM in MRO problem solving. In the modeling phase, the CM weights are determined with GA in which its fitness function is minimizing the RMSE. Then, in the optimization phase, the GA specifies the final response with the object to maximize the global desirability. Due to the fact that GA has a stochastic nature, it usually finds the response points near to optimum. Therefore, the performance the algorithm for several times will yield different responses with different GD values. This study includes a committee machine with four different ANNs. The algorithm was implemented on five case studies and the results represent for selected cases, when number of performances is equal to five, increasing in maximum GD with respect to average value of GD will be eleven percent. Increasing repeat number from five to forty-five will raise the maximum GD by only about three percent more. Consequently, the economic run number of the algorithm is five.