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Open AccessArticle

Unstable System Control Using an Improved Particle Swarm Optimization-Based Neural Network Controller

Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung City 411, Taiwan
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Electronics 2019, 8(11), 1302; https://doi.org/10.3390/electronics8111302
Received: 25 September 2019 / Revised: 30 October 2019 / Accepted: 3 November 2019 / Published: 7 November 2019
(This article belongs to the Section Computer Science & Engineering)
In this study, an improved particle swarm optimization (IPSO)-based neural network controller (NNC) is proposed for solving a real unstable control problem. The proposed IPSO automatically determines an NNC structure by a hierarchical approach and optimizes the parameters of the NNC by chaos particle swarm optimization. The proposed NNC based on an IPSO learning algorithm is used for controlling a practical planetary train-type inverted pendulum system. Experimental results show that the robustness and effectiveness of the proposed NNC based on IPSO are superior to those of other methods. View Full-Text
Keywords: neural networks; chaos; nonlinear control; inverted pendulum system; particle swarm optimization neural networks; chaos; nonlinear control; inverted pendulum system; particle swarm optimization
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Lin, C.-J.; Lin, X.-Y.; Jhang, J.-Y. Unstable System Control Using an Improved Particle Swarm Optimization-Based Neural Network Controller. Electronics 2019, 8, 1302.

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