標題: A neural fuzzy network for word information processing
作者: Lin, CT
Duh, FB
Liu, DJ
電控工程研究所
Institute of Electrical and Control Engineering
公開日期: 1-Apr-2002
摘要: A neural fuzzy system learning with fuzzy training data is proposed in this study. The system is able to process and learn numerical information as well as word information. At first, we propose a basic structure of five-layered neural network for the connectionist realization of a fuzzy inference system. The connectionist structure can house fuzzy logic rules and membership functions for fuzzy inference. The inputs, outputs, and weights of the proposed network can be fuzzy numbers of any shape. Also they can be hybrid of fuzzy numbers and numerical numbers through the use of fuzzy singletons. Based on interval arithmetics, a fuzzy supervised learning algorithm is developed for the proposed system. It extends the normal supervised learning techniques to the learning problems where only word teaching signals are available. The fuzzy supervised learning scheme can train the proposed system with desired fuzzy input-output pairs. An experimental system is constructed to illustrate the performance and applicability of the proposed scheme. (C) 2002 Elsevier Science B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/S0165-0114(01)00151-8
http://hdl.handle.net/11536/28921
ISSN: 0165-0114
DOI: 10.1016/S0165-0114(01)00151-8
期刊: FUZZY SETS AND SYSTEMS
Volume: 127
Issue: 1
起始頁: 37
結束頁: 48
Appears in Collections:Conferences Paper


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