Nonlinear System Control Using Functional-Link-Based Neuro-Fuzzy Network Model Embedded with Modified Particle Swarm Optimizer

Please use this identifier to cite or link to this item: http://ir.lib.ncut.edu.tw/handle/987654321/7254

Title:	Nonlinear System Control Using Functional-Link-Based Neuro-Fuzzy Network Model Embedded with Modified Particle Swarm Optimizer
Authors:	林正堅
Contributors:	資訊工程系
Date:	2012-03
Issue Date:	2018-05-24 09:52:56 (UTC+8)
Abstract:	This study presents an evolutionary neural fuzzy system (NFS) for nonlinear system control. The proposed NFS model uses functional link neural networks (FLNNs) as the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. A learning algorithm, which consists of structure learning and parameter learning, is presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the particle swarm optimization (PSO) algorithm, can adjust the shape of the membership function and the corresponding weighting of the FLNN. The distance-based mutation operator, which strongly encourages a global search giving the particles more chance of converging to the global optimum, is introduced. The simulation results have shown the proposed method can improve the searching ability and is very suitable for the nonlinear system control applications. © 2012 TFSA. (40 refs)
Relation:	International Journal of Fuzzy Systems
Appears in Collections:	[Department of Computer Science and Information Engineering] 【資訊工程系所】期刊論文

Files in This Item:

There are no files associated with this item.

View Licence