A Innovative Methodology to Fuzzy-Neural System Modeling
DOI:
https://doi.org/10.33193/JICIS.1.1.2022.24Keywords:
Modeling , Neural networks , Fuzzy neural networks , Fuzzy systemsAbstract
We develop simple but effective fuzzy-rule based models of complex systems from input-output data. We introduce a simple fuzzy-neural network for modeling systems, and we prove that it can represent any continuous function over a compact set. We introduce "fuzzy curves" and use them to: 1) identify significant input variables, 2) determine model structure, and 3) set the initial weights in the fuzzy-neural network model. Our method for input identification is computationally simple and, since we determine the proper network structure and initial weights in advance, we can train the network rapidly. Viewing the network as a fuzzy model gives insight into the real system, and it provides a method to simplify the neural network.
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