Identification using ANFIS with intelligent hybrid stable learning algorithm approaches

Authors:
Mahdi Aliyari Shoorehdeli;Mohammad Teshnehlab;Ali Khaki Sedigh
Affiliations:
K. N. Toosi University of Technology, Seyyed Khandan Bridge, Tehran, Iran;K. N. Toosi University of Technology, Seyyed Khandan Bridge, Tehran, Iran;K. N. Toosi University of Technology, Seyyed Khandan Bridge, Tehran, Iran
Venue:
Neural Computing and Applications
Year:
2009

Citing 0
Cited 3

Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter

Fuzzy Sets and Systems
Application of an artificial immune system-based fuzzy neural network to a RFID-based positioning system

Computers and Industrial Engineering
Application of an optimization artificial immune network and particle swarm optimization-based fuzzy neural network to an RFID-based positioning system

Information Sciences: an International Journal

Quantified Score

Hi-index	0.04

Visualization

Abstract

This paper suggests novel hybrid learning algorithm with stable learning laws for adaptive network based fuzzy inference system (ANFIS) as a system identifier and studies the stability of this algorithm. The new hybrid learning algorithm is based on particle swarm optimization (PSO) for training the antecedent part and gradient descent (GD) for training the conclusion part. Lyapunov stability theory is used to study the stability of the proposed algorithm. This paper, studies the stability of PSO as an optimizer in training the identifier, for the first time. Stable learning algorithms for the antecedent and consequent parts of fuzzy rules are proposed. Some constraints are obtained and simulation results are given to validate the results. It is shown that instability will not occur for the leaning rate and PSO factors in the presence of constraints. The learning rate can be calculated on-line and will provide an adaptive learning rate for the ANFIS structure. This new learning scheme employs adaptive learning rate that is determined by input–output data.