%0 Journal Article
%T Desirability Improvement of Committee Machine to Solve Multiple Response Optimization Problems
%A Seyed Jafar Golestaneh
%A Napsiah Ismail
%A Mohd Khairol Anuar M. Ariffin
%A Say Hong Tang
%A Hassan Moslemi Naeini
%J Advances in Artificial Neural Systems
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/628313
%X 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. 1. Introduction Multiple response optimization (MRO) problems need to find a set of input variable values (x¡¯s) which get a desired set of outputs (y's). The current study develops a proposed algorithm in recent authors' work to solve MRO problems [1]. MRO solution methodologies usually include three phases: experiments design, modeling, and optimization. There are some techniques for experiments design. Some methodologies in this phase are as follows: design of experiments (DOEs) knowledge such as factorial design and fraction factorial design, response surface methodology (RSM) such as central composite design (CCD), and Box Behnken [2, 3]. Furthermore, Taguchi orthogonal arrays [4¨C7] are derived from the Taguchi method. Modeling as the second phase is done using different mathematical or statistical models such as multiple linear and nonlinear regressions in the form of polynomials [2, 8, 9] and artificial neural networks (ANNs). Due to the existence of complicated relationship between inputs and outputs, usually ANNs are mostly used for modeling rather than for polynomials. One famous artificial neural network (ANN) is back propagation neural network (BPNN) that is used in many
%U http://www.hindawi.com/journals/aans/2013/628313/