Adaptive Neuro-Fuzzy Black-Box Modeling Based on Instrumental Variable Evolving Algorithm

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• 作者：Orlando Rocha ; Ginalber Serra
• 关键词：Evolving neuro ; fuzzy ; Takagi–Sugeno ; Black ; box modeling
• 刊名：Journal of Control, Automation and Electrical Systems
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：28
• 期：1
• 页码：50-67
• 全文大小：
• 刊物主题：Electrical Engineering; Control, Robotics, Mechatronics; Control; Robotics and Automation;
• 出版者：Springer US
• ISSN：2195-3899
• 卷排序：28

文摘

In this paper, an online identification algorithm for instrumental variable-based evolving neuro-fuzzy modeling applied to dynamic systems in noisy environment is proposed. The adopted methodology is based on neuro-fuzzy inference system with Takagi–Sugeno evolving structure, which employs an adaptive distance norm based on the maximum likelihood criterion with instrumental variable recursive parameter estimation. The application and performance analysis of the proposed algorithm is based on black-box modeling of a 2DOF Helicopter with errors in variables.