Enhance Particle’s Exploration of Particle Swarm Optimization With Individual Particle Mutation

Adsawinnawanawa, Ekkarat; Kruatrachue, Boontee; Siriboon, Kritawan

doi:10.1109/ieecon45304.2019.8939047

Cited by 5 publications

(1 citation statement)

References 4 publications

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“…global search ability, simple implementation, and good performance). Phuchan et al [30] introduced the PSO algorithm to optimize the design of backpropagation neural network (BPNN), and managed to improve the training and convergence speeds of the BPNN model. Based on the PSO, Adsawinnawanawa et al [31] carried out linear regression in the weight adjustment of multilayer feedforward network, and realized the objective of solving the global optimal weights online.…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear System Identification Method Based on Adaptive Neural Network

2021

CIT. J. comput. inf. technol

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Nonlinear system identification (NSI) is of great significance to modern scientific engineering and control engineering. Despite their identification ability, the existing analysis methods for nonlinear systems have several limitations. The neural network (NN) can overcome some of these limitations in NSI, but fail to achieve desirable accuracy or training speed. This paper puts forward an NSI method based on adaptive NN, with the aim to further improve the convergence speed and accuracy of NN-based NSI. Specifically, a generic model-based nonlinear system identifier was constructed, which integrates the error feedback and correction of predictive control with the generic model theory. Next, the radial basis function (RBF) NN was optimized by adaptive particle swarm optimization (PSO), and used to build an NSI model. The effectiveness and speed of our model were verified through experiments. The research results provide a reference for applying the adaptive PSO-optimized RBFNN in other fields.

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Section: Introductionmentioning

confidence: 99%