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基于TSK模型的模糊推理改进算法
引用本文:田一慧,钱皓,王涛.基于TSK模型的模糊推理改进算法[J].辽宁工学院学报,2009(4):255-261.
作者姓名:田一慧  钱皓  王涛
作者单位:辽宁工业大学数理科学系,辽宁锦州121001
基金项目:基金项目:辽宁省教育厅(重点实验室)基金项目(20060395)
摘    要:在传统的基于TSK模型的模糊推理算法基础上,研究了一种改进的基于TSK模型的模糊推理新算法,并应用模糊神经BP算法给出三角形隶属函数下的算法的过程,最后将新算法与传统算法做了比较,得出基于TSK模型的模糊推理新算法在实际的过程中克服了传统推理算法会出现弱连续或不连续情况的优点。

关 键 词:模糊推理  TSK模型  BP算法  神经网络

An Improved Fuzzy Reasoning Algorithm Based on TSK Model
TIAN Yi-hui,QIAN Hao,WANG Tao.An Improved Fuzzy Reasoning Algorithm Based on TSK Model[J].Journal of Liaoning Institute of Technology(Natural Science Edition),2009(4):255-261.
Authors:TIAN Yi-hui  QIAN Hao  WANG Tao
Affiliation:(Dept.of Mathematics & Physics, Liaoning University of Technology, Jinzhou 121001, China)
Abstract:For TSK fuzzy reasoning model with two linguistic variables, two inputs and one output. If the inference antecedents is Triangular-type membership functions. By using back-propagation learning algorithm to learn and adjust the parameters of the function membership in the fuzzy reasoning rules, the conventional neuro-fuzzy reasoning algorithms and the improved neuro-fuzzy reasoning algorithms are proposed, respectively. Finally, some comparisons between these two algorithms are made. The main advantages of the improved neuro-fuzzy reasoning algorithms were that the case of weak-firing or non-firing was all avoied, which occurred from the traditional approach.
Keywords:fuzzy reasoning  TSK model  back-propagation algorithm  neuarl network
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