标题: A new data-driven neural fuzzy system with collaborative fuzzy clustering mechanism
作者: Prasad, M.
Lin, Y. Y.
Lin, C. T.
Er, M. J.
Prasad, O. K.
资讯工程学系
电机工程学系
脑科学研究中心
Department of Computer Science
Department of Electrical and Computer Engineering
Brain Research Center
关键字: Neural networks;Fuzzy system;Big-data;Privacy and security;Collaborative technique;On-line learning system;Time series prediction
公开日期: 1-十一月-2015
摘要: In this paper, a novel fuzzy rule transfer mechanism for self-constructing neural fuzzy inference networks is being proposed. The features of the proposed method, termed data-driven neural fuzzy system with collaborative fuzzy clustering mechanism (DDNFS-CFCM) are; (1) Fuzzy rules are generated facilely by fuzzy c-means (FCM) and then adapted by the preprocessed collaborative fuzzy clustering (PCFC) technique, and (2) Structure and parameter learning are performed simultaneously without selecting the initial parameters. The DDNFS-CFCM can be applied to deal with big data problems by the virtue of the PCFC technique, which is capable of dealing with immense datasets while preserving the privacy and security of datasets. Initially, the entire dataset is organized into two individual datasets for the PCFC procedure, where each of the dataset is clustered separately. The knowledge of prototype variables (cluster centers) and the matrix of just one halve of the dataset through collaborative technique are deployed. The DDNFS-CFCM is able to achieve consistency in the presence of collective knowledge of the PCFC and boost the system modeling process by parameter learning ability of the self-constructing neural fuzzy inference networks (SONFIN). The proposed method outperforms other existing methods for time series prediction problems. (C) 2015 Elsevier B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/j.neucom.2015.04.034
http://hdl.handle.net/11536/127968
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2015.04.034
期刊: NEUROCOMPUTING
Volume: 167
起始页: 558
结束页: 568
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