@Article{2019.100000093,
AUTHOR = {Ching‐Yi Chen, Yi‐Jen Lin},
TITLE = {Development of a Data‐Driven ANFIS Model by Using PSO‐LSE Method for  Nonlinear System Identification},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {25},
YEAR = {2019},
NUMBER = {2},
PAGES = {319--327},
URL = {http://www.techscience.com/iasc/v25n2/39659},
ISSN = {2326-005X},
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.},
DOI = {10.31209/2019.100000093}
}