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http://dx.doi.org/10.5391/JKIIS.2011.21.1.12

Architectural Analysis of Type-2 Interval pRBF Neural Networks Using Space Search Evolutionary Algorithm  

Oh, Sung-Kwun (수원대학교 전기공학과)
Kim, Wook-Dong (수원대학교 전기공학과)
Park, Ho-Sung (수원대학교 전기공학과)
Lee, Young-Il (수원대학교 전기공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.21, no.1, 2011 , pp. 12-18 More about this Journal
Abstract
In this paper, we proposed Interval Type-2 polynomial Radial Basis Function Neural Networks. In the receptive filed of hidden layer, Interval Type-2 fuzzy set is used. The characteristic of Interval Type-2 fuzzy set has Footprint Of Uncertainly(FOU), which denotes a certain level of robustness in the presence of un-known information when compared with the type-1 fuzzy set. In order to improve the performance of proposed model, we used the linear polynomial function as connection weight of network. The parameters such as center values of receptive field, constant deviation, and connection weight between hidden layer and output layer are optimized by Conjugate Gradient Method(CGM) and Space Search Evolutionary Algorithm(SSEA). The proposed model is applied to gas furnace dataset and its result are compared with those reported in the previous studies.
Keywords
Conjugate Gradient Method;
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Times Cited By KSCI : 1  (Citation Analysis)
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