Article ID: | iaor2004767 |
Country: | United Kingdom |
Volume: | 44 |
Issue: | 12 |
Start Page Number: | 1503 |
End Page Number: | 1514 |
Publication Date: | Dec 2002 |
Journal: | Computers & Mathematics with Applications |
Authors: | Lee E.S., Cheng Chi-Bin, Cheng C.-J. |
Keywords: | programming: network |
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 issued to optimise the resulting multiple objective decision making problem. A chemical process whose function is represented approximately in tabular form is solved to illustrate the approach.