TY - EJOU AU - Chen, Ching‐Yi AU - Lin, Yi‐Jen TI - Development of a Data‐Driven ANFIS Model by Using PSO‐LSE Method for Nonlinear System Identification T2 - Intelligent Automation \& Soft Computing PY - 2019 VL - 25 IS - 2 SN - 2326-005X AB - 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. KW - ANFIS KW - competitive learning scheme KW - PSO-LSE method DO - 10.31209/2019.100000093