Rule Generation for Hierarchical Fuzzy Systems
نویسنده
چکیده
In this paper a new method of rule generation for hierarchical fuzzy systems (Hierarchical Fuzzy Associative Memory, HIFAM) is described. A HIFAM is structured as a binary tree and overcomes the exponential growth of the rulebases when the number of inputs increases. The training algorithm for HIFAM is suited for approximation and classification problems. Several benchmarks demonstrate that the proposed method compares well with existing learning techniques like artificial neural networks or decision trees.
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تاریخ انتشار 1997