Fuzzy assisted learning in backpropagation neural networks |
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Authors: | H. O. Nyongesa |
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Affiliation: | (1) Department of Information Systems and Computing, Brunel University, UB8 3PH Uxbridge, Middlesex, UK |
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Abstract: | This paper reports on studies to overcome difficulties associated with setting the learning rates of backpropagation neural networks by using fuzzy logic. Building on previous research, a fuzzy control system is designed which is capable of dynamically adjusting the individual learning rates of both hidden and output neurons, and the momentum term within a back-propagation network. Results show that the fuzzy controller not only eliminates the effort of configuring a global learning rate, but also increases the rate of convergence in comparison with a conventional backpropagation network. Comparative studies are presented for a number of different network configurations. The paper also presents a brief overview of fuzzy logic and backpropagation learning, highlighting how the two paradigms can enhance each other. |
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Keywords: | Adaptive algorithms Back propagation Fuzzy control Improved learning Neural networks |
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