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FUNCOM: A constrained learning algorithm for fuzzy neural networks
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DOI：10.1016/S0165-0114(97)00363-1.png)
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Abstract
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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 modifies 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 effectiveness of the proposed algorithm. (C) 2000 Published by Elsevier Science B.V. All rights reserved.
Keywords:
artificial intelligence
linguistic modeling
constrained optimization
structure/parameter learning
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