A Robust Recurrent Wavelet Neural Network Controller With Improved Particle Swarm Optimization for Linear Synchronous Motor Drive

2008 ◽  
Vol 23 (6) ◽  
pp. 3067-3078 ◽  
Author(s):  
Faa-Jeng Lin ◽  
Li-Tao Teng ◽  
Hen Chu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Synchronous Motor ◽  
Wavelet Neural Network ◽  
Motor Drive ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
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 Bus Arrival Time Prediction Using Wavelet Neural Network Trained by Improved Particle Swarm Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Yuanwen Lai ◽  
Said Easa ◽  
Dazu Sun ◽  
Yian Wei
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Arrival Time ◽  
Particle Swarm ◽  
Wavelet Neural Network ◽  
Local Optimum ◽  
Gradient Descent Method ◽  
Maximum Relative Error ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

Prediction of bus arrival time is an important part of intelligent transportation systems. Accurate prediction can help passengers make travel plans and improve travel efficiency. Given the nonlinearity, randomness, and complexity of bus arrival time, this paper proposes the use of a wavelet neural network (WNN) model with an improved particle swarm optimization algorithm (IPSO) that replaces the gradient descent method. The proposed IPSO-WNN model overcomes the limitations of the gradient-based WNN which can easily produce local optimum solutions and stop the training process and thus improves prediction accuracy. Application of the model is illustrated using operational data of an actual bus line. The results show that the proposed model is capable of accurately predicting bus arrival time, where the root-mean square error and the maximum relative error were reduced by 42% and 49%, respectively.

scholarly journals A Novel Approach for Shearer Cutting Load Identification through Integration of Improved Particle Swarm Optimization and Wavelet Neural Network

2014 ◽  
Vol 6 ◽  
pp. 521629 ◽  
Author(s):  
Zhongbin Wang ◽  
Lei Si ◽  
Chao Tan ◽  
Xinhua Liu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Wavelet Neural Network ◽  
Population Diversity ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Novel Approach ◽  
Comparison Results

In order to accurately identify the change of shearer cutting load, a novel approach was proposed through integration of improved particle swarm optimization and wavelet neural network. An improved updating strategy for inertia weight was presented to avoid falling into the local optimum. Moreover, immune mechanism was applied in the proposed approach to enhance the population diversity and improve the quality of solution, and the flowchart of the proposed approach was designed. Furthermore, a simulation example was carried out and comparison results indicated that the proposed approach was feasible, efficient, and outperforming others. Finally, an industrial application example of coal mining face was demonstrated to specify the effect of the proposed system.

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.

Sign in / Sign up

Export Citation Format

Share Document