Abstract
Modeling and optimization of a process with multiple outputs is discussed in this pa- per. A neuro-fuzzy system named MANFIS, which comprises a fuzzy inference structure and neural network learning ability, is used to model a multiple output process. Optimization of such a process is formulated as a multiple objective decision making problem, and a genetic algorithm and a numerical method are introduced, respectively, to solve this problem based on the MAN- FIS model. We have used these two algorithms, respectively, to solve a chemical process opti- mization problem, and compared their performances. A combination of these two algorithms is also suggested to improve performances of both algorithms. The proposed approach is also ap- plied to a wire-bonding problem in semiconductor manufacturing.
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