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Development of a Data‐Driven ANFIS Model by Using PSO‐LSE Method for Nonlinear System Identification

Ching‐Yi Chen, Yi‐Jen Lin

Department of Information and Telecommunications Engineering, Ming Chuan University, Taoyuan, Taiwan, ROC

* Corresponding Author: Ching‐Yi Chen, email

Intelligent Automation & Soft Computing 2019, 25(2), 319-327. https://doi.org/10.31209/2019.100000093

Abstract

In this study, a systematic data-driven adaptive neuro-fuzzy inference system (ANFIS) modelling methodology is proposed. The new methodology employs an unsupervised competitive learning scheme to build an initial ANFIS structure from input-output data, and a high-performance PSO-LSE method is developed to improve the structure and to identify the consequent parameters of ANFIS model. This proposed modelling approach is evaluated using several nonlinear systems and is shown to outperform other modelling approaches. The experimental results demonstrate that our proposed approach is able to find the most suitable architecture with better results compared with other methods from the literature.

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APA Style
Chen, C., Lin, Y. (2019). Development of a data‐driven ANFIS model by using PSO‐LSE method for nonlinear system identification. Intelligent Automation & Soft Computing, 25(2), 319-327. https://doi.org/10.31209/2019.100000093
Vancouver Style
Chen C, Lin Y. Development of a data‐driven ANFIS model by using PSO‐LSE method for nonlinear system identification. Intell Automat Soft Comput . 2019;25(2):319-327 https://doi.org/10.31209/2019.100000093
IEEE Style
C. Chen and Y. Lin, "Development of a Data‐Driven ANFIS Model by Using PSO‐LSE Method for Nonlinear System Identification," Intell. Automat. Soft Comput. , vol. 25, no. 2, pp. 319-327. 2019. https://doi.org/10.31209/2019.100000093



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