Abstract
Optimization of a multiple output system, whose function is only approximately known and is represented in tabular form, is modeled and optimized by the combined use of a neuro-fuzzy network and optimization techniques which do not require the explicit representation of the function. Neuro-fuzzy network is useful for learning the approximate original tabular system. However, the results obtained by the neuro-fuzzy network are represented implicitly in the network. The MANFIS neuro-fuzzy network, which is an extension of the ANFIS network, is used to model the multiple output system and a genetic algorithm is used to optimize the resulting multiple objective decision making problem. A chemical process whose function is represented approximately in tabular form is solved to illustrate the approach.
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