Learning algorithms and the inversion of fuzzy neural networks = 퍼지 신경망의 학습 알고리즘과 역연산에 관한 연구

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The learning algorithm of fuzzy neural networks deserves much attention because it determines the learning capability and the performance of the networks. Several learning algorithms have been proposed, but there is still no algorithm that is accepted as the standard solution. We make an analysis of those algorithms in a comprehensive perspective and incorporate them into one learning procedure. To achieve a particular learning algorithm, we choose an adjusting scheme for weight factors, which specifies how the weight factors are represented internally. We also formulate an inversion algorithm for fuzzy neural networks. The inversion yields an estimate inverse of a given target in a fuzzy neural network. It is based on the gradient descent search and employs the strategy used by the learning procedure in adjusting weight factors. We conduct experiments on the parity-3 problem to show how the learning procedure and the inversion algorithm work in reliably-trained networks. We also demonstrate that the technique of inversion can be used for better examination of fuzzy neural networks.
Advisors
Han, Tai-Sookresearcher한태숙researcher
Description
한국과학기술원 : 전산학과,
Publisher
한국과학기술원
Issue Date
1998
Identifier
134015/325007 / 000963237
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전산학과, 1998.2, [ vi, [54] p. ]

Keywords

Inversion; Fuzzy neural network; Learning algorithm; 학습 알고리즘; 역연산; 퍼지 신경망

URI
http://hdl.handle.net/10203/34262
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=134015&flag=dissertation
Appears in Collection
CS-Theses_Master(석사논문)
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