RESPONSE-SURFACE METHODOLOGY - A NEURAL-NETWORK APPROACH

Response surface methodology is used to optimize the parameters of a p rocess when the function that describes it is unknown, The procedure i nvolves fitting a function to the given data and then using optimizati on techniques to obtain the optimal parameters. This procedure is usua lly difficult due to the fact that obtaining the right model may not b e possible or at best very time consuming. In this paper, a two-stage procedure for obtaining the best parameters for a process with an unkn own model is developed. The procedure is based on implementing respons e surface methodology via neural networks, Two neural networks are tra ined: one for the unknown function and the other for derivatives of th is function which are computed using the first neural network. These n eural networks are then used iteratively to compute parameters for an equation which is ultimately used for optimizing the function, Results of some simulation studies are also presented. (C) 1997 Elsevier Scie nce B.V.