The Use of Global Best Position in Rerun of Particle Swarm Optimization

Cheypoca, Varothon; Siriboon, Kritawan; Kruatrachue, Boontee

doi:10.1109/iceast.2018.8434504

Cited by 6 publications

(1 citation statement)

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“…The traditional ANN can effectively solve some NSI problems, but fail to realize ideal accuracy or training speed [28,29]. Inspired by the foraging behavior of bird flocks, the particle swarm optimization (PSO) has been developing rapidly in recent years, owing to its advantages in complex NSI (e.g.…”

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%