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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,

Intelligent Automation & Soft Computing 2019, 25(2), 319-327.


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.


Cite This Article

C. Chen and Y. Lin, "Development of a data‐driven anfis model by using pso‐lse method for nonlinear system identification," Intelligent Automation & Soft Computing, vol. 25, no.2, pp. 319–327, 2019.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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