Rule weights in a neuro-fuzzy system with a hierarchical domain partition
International Journal of Applied Mathematics and Computer Science
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The paper presents the hierarchical domain partition in the neuro-fuzzy system with parameterized consequences. The hierarchical domain partition has the advantages of grid partition and clustering. It avoids the curse of dimensionality and the problem of determination of number of regions. This method of domain partition reduces the occurrence of areas with low membership to all regions. The paper depicts the iterative procedure of hierarchical split based on finding and splitting the region with the highest contribution to the error of the system. The split of regions into two subregions in the proposed system is based on the fuzzy clustering, resulting in both splitting and fuzzyfication of the subregions. Both decisive and error values are taken into consideration in splitting the regions. The paper presents the results of experiments on real life and synthetic datasets. This approach can produce neuro-fuzzy inference systems with better generalisation ability and subsequently lower error rate.