标题:A fuzzy process neural network model and its application in process signal classification
作者:Xu, Shaohua; Liu, Kun; Li, Xuegui
作者机构:[Xu, Shaohua; Liu, Kun; Li, Xuegui] Shandong Univ Sci & Technol, Coll Comp Sci & Engn, Qingdao, Peoples R China.
通讯作者:Liu, Kun;Liu, K
通讯作者地址:[Liu, K]Shandong Univ Sci & Technol, Coll Comp Sci & Engn, Qingdao, Peoples R China.
来源:NEUROCOMPUTING
出版年:2019
卷:335
页码:1-8
DOI:10.1016/j.neucom.2019.01.050
关键词:Process signal classification; Fuzzy decision; Fuzzy process neural; network; Training algorithm; Small sample modeling
摘要:Aiming at process signal classification combined with fuzzy decision rules, a fuzzy process neural network (FPNN) is proposed in this paper. The FPNN is structured with a process signal input layer, a fuzzy process neuron (FPN) hidden layer, a signal pattern layer, and a fuzzy decision output layer. The spatio-temporal aggregation in the FPN is taken as a generalized inner product operation, which can measure the fuzzy similarity of the distributed features among process signals and FPN uses an exponential fuzzy membership function as activation function. The FPNN selectively sums the output of the FPN hidden layer to the pattern layer according to the category of input signal. By linking a Takagi-Sugeno fuzzy classifier behind the pattern layer, direct classification of the process signals is achieved. The FPNN can improve the deficiencies of existing time-varying signal classification methods, such as the complete training data sets are required, and the information processing processes and algorithms are complicated. In this paper, the theoretical properties of the FPNN are analyzed, and the comprehensive learning algorithm for FPNNs is given. The discrimination of reservoir water flooding condition based on multi-channel well logging process signals was used as an example for experimental analysis; the results verify the validity of the model and algorithm. (C) 2019 Elsevier B.V. All rights reserved.
收录类别:EI;SCIE
资源类型:期刊论文
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