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FUNCOM: A constrained learning algorithm for fuzzy neural networks

✍ Scribed by Paris Mastorocostas; John Theocharis

Publisher
Elsevier Science
Year
2000
Tongue
English
Weight
384 KB
Volume
112
Category
Article
ISSN
0165-0114

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✦ Synopsis

A novel learning algorithm, the FUNCOM (Fuzzy Neural Constrained Optimization Method) is suggested in this paper, for training fuzzy neural networks. The training task is formulated as a constrained optimization problem, whose objective is twofold: (i) minimization of an error measure, leading to successful approximation of the input=output mapping and (ii) optimization of an additional functional, which aims at formulating suitable internal representations of the fuzzy model. Optimization of the above functionals is carried out under the constraints imposed by the fuzzy system, which appear in the form of state equations. A fuzzy adaptation scheme is also suggested, which continuously modiΓΏes the step size during training, with the scope to improve the learning attributes of the algorithm. The FUNCOM qualities are investigated by a series of simulation examples. Comparisons with other learning algorithms are given and discussed, indicating the e ectiveness of the proposed algorithm.

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