Ching‐Yi Chen, Yi‐Jen Lin
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 319-327, 2019, DOI: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. More >
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