A Novel Interactively Recurrent Self-evolving Fuzzy CMAC and Its Classification Applications

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题名:	A Novel Interactively Recurrent Self-evolving Fuzzy CMAC and Its Classification Applications
作者:	Jyun-Guo Wang, Shen-Chuan Tai, Cheng-Jian Lin
贡献者:	圖書館
关键词:	Recurrent network cerebellar model articulation controller fuzzy theory gradient descent algorithm classification
日期:	2015-09
上传时间:	2016-10-20 15:36:31 (UTC+8)
摘要:	In this paper, an Interactively Recurrent Self-evolving Fuzzy Cerebellar Model Articulation Controller (IRSFCMAC) model is developed for solving classification problems. The proposed IRSFCMAC classifier consists of internal feedback and external loops, which are generated by the hypercube cell firing strength to itself and other hypercube cells. The learning process of the IRSFCMAC gets started with an empty hypercube base, and then all of hypercube cells are generated and learned online via structure and parameter learning, respectively. The structure learning algorithm is based on the degree measure to determine the number of hypercube cells. The parameter learning algorithm, based on the gradient descent method, adjusts the shapes of the membership functions and the corresponding fuzzy weights of the IRSFCMAC. Finally, the proposed IRSFCMAC model is tested by four benchmark classification problems. Experimental results show that the proposed IRSFCMAC model has superior performance than traditional FCMAC and other models.
關聯:	International Journal of Computational Intelligence and Applications, Volume 14, Issue 03
显示于类别:	[資訊工程系(所)] 【資訊工程系所】期刊論文

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