Nonlinear system control using functional-link-based neuro-fuzzy network model embedded with modified particle swarm optimizer

Publication Type:
Journal Article
Citation:
International Journal of Fuzzy Systems, 2012, 14 (1), pp. 97 - 109
Issue Date:
2012-03-01
Full metadata record
Files in This Item:
Filename Description Size
ContentServer (37).pdfPublished Version364.24 kB
Adobe PDF
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.
Please use this identifier to cite or link to this item: