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http://dx.doi.org/10.5391/JKIIS.2007.17.7.970

The Design of Polynomial Network Pattern Classifier based on Fuzzy Inference Mechanism and Its Optimization  

Kim, Gil-Sung (수원대학교 전기공학과)
Park, Byoung-Jun ((주)지엠테크 기술연구소)
Oh, Sung-Kwun (수원대학교 전기공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.17, no.7, 2007 , pp. 970-976 More about this Journal
Abstract
In this study, Polynomial Network Pattern Classifier(PNC) based on Fuzzy Inference Mechanism is designed and its parameters such as learning rate, momentum coefficient and fuzzification coefficient are optimized by means of Particle Swarm Optimization. The proposed PNC employes a partition function created by Fuzzy C-means(FCM) clustering as an activation function in hidden layer and polynomials weights between hidden layer and output layer. Using polynomials weights can help to improve the characteristic of the linear classification of basic neural networks classifier. In the viewpoint of linguistic analysis, the proposed classifier is expressed as a collection of "If-then" fuzzy rules. Namely, architecture of networks is constructed by three functional modules that are condition part, conclusion part and inference part. The condition part relates to the partition function of input space using FCM clustering. In the conclusion part, a polynomial function caries out the presentation of a partitioned local space. Lastly, the output of networks is gotten by fuzzy inference in the inference part. The proposed PNC generates a nonlinear discernment function in the output space and has the better performance of pattern classification as a classifier, because of the characteristic of polynomial based fuzzy inference of PNC.
Keywords
Pattern Recognition; Pattern Classifier; Neural Networks; Polynomial Neural Networks; Radial Basis Function Neural Networks; Polynomial Network Pattern Classifier;
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