Identifying a fuzzy model by using the bipartite membership functions

The gradient descent method and genetic algorithms have been widely used to refine fuzzy models constructed for the given data. This paper approaches from another viewpoint to adjusting the fuzzy model built for the preproces sed data, The membership functions defined for each premise variable are eq ually distributed and fixed in the transformed domain, To better identify t he fuzzy model, either the transformation functions or consequent parts of fuzzy rules or both need to be optimized. Since adjusting a rule to satisfy one pattern may deteriorate the others performance and result in a lengthy tuning process, we then treat each triangular membership function as two d isjoint ones such that each fuzzy rule is divided into mutually independent rules. This in turn benefits the refinement of consequent parts in the fuz zy rules since adjusting a rule will not affect the others. Not only the si mulation results from the proposed model will be demonstrated but also the results from the conventional approaches will be given for comparisons. We use the least squared method to calculate the desired consequent real numbe rs for the data located in the same region of transformed domain. The confo rmity of the after-tuned consequent parts in the fuzzy rules with the desir ed values further verifies the effectiveness of the presented methodology. (C) 2001 Elsevier Science B.V. All rights reserved.