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An Automatic Adjustment Method of Backpropagation Learning Parameters, Using Fuzzy Inference
Fumio UENO Takahiro INOUE Kenichi SUGITANI Badur-ul-Haque BALOCH Takayoshi YAMAMOTO
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 1993/04/25
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Neural Networks
neural networks, backpropagation, fuzzy theory,
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In this work, we introduce a fuzzy inference in conventional backpropagation learning algorithm, for networks of neuron like units. This procedure repeatedly adjusts the learning parameters and leads the system to converge at the earliest possible time. This technique is appropriate in a sense that optimum learning parameters are being applied in every learning cycle automatically, whereas the conventional backpropagation doesn't contain any well-defined rule regarding the proper determination of the value of learning parameters.