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Neural-Network-Based Fuzzy Logic Control and Decision System
December 1991 (vol. 40 no. 12)
pp. 1320-1336

A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed. This connectionist model, in the form of feedforward multilayer net, combines the idea of fuzzy logic controller and neural-network structure and learning abilities into an integrated neural-network-based fuzzy logic control and decision system. A fuzzy logic control decision network is constructed automatically by learning the training examples itself. By combining both unsupervised (self-organized) and supervised learning schemes, the learning speed converges much faster than the original backpropagation learning algorithm. The connectionist structure avoids the rule-matching time of the inference engine in the traditional fuzzy logic system. Two examples are presented to illustrate the performance and applicability of the proposed model.

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Index Terms:
neural network based fuzzy logic control; connectionist model; decision system; feedforward multilayer net; learning; backpropagation; inference engine; performance; artificial intelligence; decision theory; fuzzy logic; inference mechanisms; learning systems; neural nets.
C.-T. Lin, C.S.G. Lee, "Neural-Network-Based Fuzzy Logic Control and Decision System," IEEE Transactions on Computers, vol. 40, no. 12, pp. 1320-1336, Dec. 1991, doi:10.1109/12.106218
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