RBFNs Nonlinear Control System Design through BFPSO Algorithm

Hsuan Ming Feng; Ching Chang Wong; Ji Hwei Horng

doi:10.4028/www.scientific.net/AMM.764-765.619

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 764-765RBFNs Nonlinear Control System Design through...

RBFNs Nonlinear Control System Design through BFPSO Algorithm

Abstract:

All parameters are automatically extracted by the bacterial foraging particle swarm optimization (BFPSO) algorithm to approach the desired control system. Three parameterize basis function neural networks (RBFNs) model to solve the car-pole system problem. Several free parameters of radial basis functions can be automatically tuned by the direct of the specified fitness function. In additional, the proper number of radial basis functions (RBFs) of the constructed RBFNs can be chosen by the defined fitness function which takes this factor into account. The desired multiple objectives of the RBFNs control system are proposed to simultaneously approach the smaller errors with a fewer RBFs number. Simulations show that the developed RBFNs control systems efficiently achieve the desired the setting lot as soon as possible.

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Periodical:

Applied Mechanics and Materials (Volumes 764-765)

Pages:

619-623

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.764-765.619

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Online since:

May 2015

Authors:

Hsuan Ming Feng*, Ching Chang Wong, Ji Hwei Horng

Keywords:

BFPSO, Nonlinear Control, RBFNs

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* - Corresponding Author

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