Title :
Online ANFIS controller based on RBF identification and PSO
Author :
Farid, Amro M. ; Barakati, S. Masoud ; Seifipour, Navid ; Tayebi, Navid
Author_Institution :
Sch. of Electr. & Comput. Eng., Univ. of Sistan & Baluchestan, Zahedan, Iran
Abstract :
Adaptive neuro-fuzzy inference system (ANFIS) is combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system, capable of reasoning and learning in an uncertain and imprecise environment. In this paper online training of ANFIS is done using radial basis function (RBF) neural network. In this online approach, identification of controlled plant is done, and based on this identification, the weights and coefficients are adjusted timely. Finally, to overcome initialization problem, using Particle swarm optimization (PSO) as an evolutionary algorithm is proposed.
Keywords :
adaptive control; evolutionary computation; fuzzy control; fuzzy neural nets; fuzzy reasoning; identification; learning systems; neurocontrollers; particle swarm optimisation; radial basis function networks; uncertain systems; ANFIS online training; PSO; RBF identification; RBF neural network; adaptive neuro-fuzzy inference system; controlled plant identification; evolutionary algorithm; hybrid neuro-fuzzy system; imprecise environment; initialization problem; learning; online ANFIS controller; particle swarm optimization; radial basis function neural network; reasoning; uncertain environment; Biological neural networks; Educational institutions; Jacobian matrices; Mathematical model; Nonlinear systems; Training; ANFIS; PSO; RBF identification; online neuro-fuzzy controller;
Conference_Titel :
Control Conference (ASCC), 2013 9th Asian
Conference_Location :
Istanbul
Print_ISBN :
978-1-4673-5767-8
DOI :
10.1109/ASCC.2013.6606232
