Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
497408 | Applied Soft Computing | 2009 | 13 Pages |
Abstract
An algorithm for the generation of a TS-type neuro-fuzzy system is presented. There are two stages in the generation: in the first stage, an initial structure adapted from an empty neuron or fuzzy rule set, based on the geometric growth criterion and the É-completeness of fuzzy rules; in the second stage, the obtained initial structure is refined by a hybrid learning algorithm based on backpropagation and a proposed recursive weight learning algorithm to minimize the system error. The similarity analysis applied throughout the entire learning process attempts both to alleviate overlap among membership functions and to reduce the complexity of the obtained system. Benchmark examples, comparing the proposed algorithm with previous approaches, show the proposed algorithm is more effective in terms of both model accuracy and compactness.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Gang Leng, Xiao-Jun Zeng, John A. Keane,