基于改进自适应神经模糊推理模型的
回流焊参数设定方法

上海交通大学学报（自然版） ›› 2011, Vol. 45 ›› Issue (12): 1741-1746.

基于改进自适应神经模糊推理模型的
回流焊参数设定方法

沙建军，潘尔顺

(上海交通大学机械与动力工程学院，上海 200240)

收稿日期:2010-10-25 出版日期:2011-12-31 发布日期:2011-12-31
基金资助:
国家自然科学基金资助项目（50875168）

Soldering Parameter-Setting Approach Based on Improved Adaptive Neuro Fuzzy Inference Model

SHA Jian-Jun, PAN erShun

(School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240, China)

Received:2010-10-25 Online:2011-12-31 Published:2011-12-31

摘要/Abstract

摘要： 复杂制造过程中，系统参数往往呈现动态、非线性特征，以回流焊过程为例，针对现有参数设定方法效率和精度不足的问题，提出了一种基于改进自适应神经模糊推理模型的在线参数设定方法.引入了神经网络预测器来增强模糊神经网络的自适应能力和非线性逼近能力，通过最近邻聚类算法对模糊规则的参数和结构进行调整和在线更新，以提高模糊推理的精度，增强系统的容错性和鲁棒性.以回流焊过程参数设定过程为背景，建立仿真实验.结果表明，所提出方法可有效实现非线性输入输出系统参数的快速及准确的设定.

关键词: 自适应神经模糊推理系统, 预测器, 最近邻聚类算法, 回流焊, 在线参数设定

Abstract: To solve the problem in setting parameters for nonlinear dynamic system, an adaptive neuro fuzzy inference system was adopted to model the dynamic I/O system. A neural network predictor was introduced to enhance the adaptive ability of fuzzy neural networks and nonlinear approximation ability. By virtue of the nearest neighbor clustering algorithm, the parameters and the structure of the fuzzy rules were adjusted and updated, which improves the accuracy of fuzzy reasoning and enhances the system’s fault tolerance and robustness. The simulation experiment’s result shows that the proposed method can effectively achieve the quick and accurate setting of the nonlinear I/O system parameters.

Key words: adaptive neuro fuzzy inference system(ANFIS)； neural network predictor(NNP), nearest neighbor clustering algorithm(NNCA), soldering, online parameter setting

中图分类号:

F253.3

沙建军, 潘尔顺. 基于改进自适应神经模糊推理模型的
回流焊参数设定方法[J]. 上海交通大学学报（自然版）, 2011, 45(12): 1741-1746.

SHA Jian-Jun, PAN erShun. Soldering Parameter-Setting Approach Based on Improved Adaptive Neuro Fuzzy Inference Model[J]. Journal of Shanghai Jiaotong University, 2011, 45(12): 1741-1746.

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