Designing multi-layer quantum neural network controller for chaos control of rod-type plasma torch system using improved particle swarm optimization

2018 ◽  
Vol 10 (3) ◽  
pp. 317-331 ◽  
Author(s):  
Esmaeil Salahshour ◽  
Milad Malekzadeh ◽  
Reza Gholipour ◽  
Saeed Khorashadizadeh
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Plasma Torch ◽  
Chaos Control ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Quantum Neural Network ◽  
Neural Network Controller ◽  
Network Controller

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

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1302 ◽  
Author(s):  
Cheng-Jian Lin ◽  
Xin-You Lin ◽  
Jyun-Yu Jhang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Pendulum System ◽  
Improved Particle Swarm Optimization ◽  
Inverted Pendulum System ◽  
Neural Network Controller ◽  
Network Controller

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.

scholarly journals Tuning Artificial Neural Network Controller Using Particle Swarm Optimization Technique for Nonlinear System

2021 ◽  
Author(s):  
Sabrine Slama ◽  
Ayachi Errachdi ◽  
Mohamed Benrejeb
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Nonlinear System ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Neural Network Controller ◽  
Network Controller ◽  
Artificial Neural

This chapter proposes an optimization technique of Artificial Neural Network (ANN) controller, of single-input single-output time-varying discrete nonlinear system. A bio-inspired optimization technique, Particle Swarm Optimization (PSO), is proposed to be applied in ANN to avoid any possibilities from local extreme condition. Further, a PSO based neural network controller is also developed to be integrated with the designed system to control a nonlinear systems. The simulation results of an example of nonlinear system demonstrate the effectiveness of the proposed approach using Particle Swarm Optimization approach in terms of reduced oscillations compared to classical neural network optimization method. MATLAB was used as simulation tool.

Training Back-Propagation Neural Network for Target Localization Using Improved Particle Swarm Optimization

2013 ◽  
Vol 333-335 ◽  
pp. 1384-1387
Author(s):  
Jin Jie Yao ◽  
Xiang Ju ◽  
Li Ming Wang ◽  
Jin Xiao Pan ◽  
Yan Han
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Back Propagation ◽  
Particle Swarm ◽  
Back Propagation Neural Network ◽  
Position Estimation ◽  
Target Localization ◽  
Prediction Ability ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

Target localization technology has been intensively studied and broadly applied in many fields. This paper presents one improved particle swarm optimization technique in training a back-propagation neural network for position estimation in target localization. The proposed scheme combines particle swarm optimization (PSO), back-propagation neural network (BP), adaptive inertia weight and hybrid mutation, called IPSO-BP. To verify the proposed IPSO-BP approach, comparisons between the PSO-based BP approach (PSO-BP) and general back-propagation neural network (BP) are made. The computational results show that the proposed IPSO-BP approach exhibits much better performance in the training process and better prediction ability in the validation process than those using the other two base line approaches.

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