Function approximation using fuzzy neural networks with robust learning algorithm

dc.contributor國立臺灣師範大學電機工程學系zh_tw
dc.contributor.authorW.-Y. Wangen_US
dc.contributor.authorT.-T. Leeen_US
dc.contributor.authorC.-L. Liuen_US
dc.contributor.authorC.-H. Wangen_US
dc.date.accessioned2014-10-30T09:28:17Z
dc.date.available2014-10-30T09:28:17Z
dc.date.issued1997-08-01zh_TW
dc.description.abstractThe paper describes a novel application of the B-spline membership functions (BMF's) and the fuzzy neural network to the function approximation with outliers in training data. According to the robust objective function, we use gradient descent method to derive the new learning rules of the weighting values and BMF's of the fuzzy neural network for robust function approximation. In this paper, the robust learning algorithm is derived. During the learning process, the robust objective function comes into effect and the approximated function will gradually be unaffected by the erroneous training data. As a result, the robust function approximation can rapidly converge to the desired tolerable error scope. In other words, the learning iterations will decrease greatly. We realize the function approximation not only in one dimension (curves), but also in two dimension (surfaces). Several examples are simulated in order to confirm the efficiency and feasibility of the proposed approach in this paperen_US
dc.description.urihttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=604123zh_TW
dc.identifierntnulib_tp_E0604_01_049zh_TW
dc.identifier.issn1083-4419�zh_TW
dc.identifier.urihttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/31971
dc.languageenzh_TW
dc.publisherIEEE Systems, Man, and Cybernetics Societyen_US
dc.relationIEEE Transactions on Systems, Man, And Cybernetics-Part B, 27(4), 740-747.en_US
dc.subject.otherFunction approximationen_US
dc.subject.otherfuzzy neural networken_US
dc.subject.otherrobust learning algorithm.en_US
dc.titleFunction approximation using fuzzy neural networks with robust learning algorithmen_US

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