Optimization of Non-fuzzy Neural Networks Based on Crisp Rules in Scatter Partition
We introduce a design of non-fuzzy neural networks that have crisp rules in scatter partition. To generate the crisp rules and construct the networks, we use hard c-means clustering algorithm. The partitioned local spaces indicate the crisp rules of the proposed networks. The consequence part of the rule is represented by polynomial functions. The coefficients of the polynomial functions are learned using back-propagation algorithm. In order to optimize the parameters of the proposed networks we use particle swarm optimization techniques. The proposed networks are evaluated with the example for nonlinear process.
KeywordsNon-fuzzy neural networks (NFNNs) Crisp rules Scatter partition Hard C-means clustering Particle swarm optimization
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2011835).
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