Facial Expression Classification with Enhanced Feature Extraction using Particle Swarm Optimization and Emotional Back Propagation Neural Network

2016 ◽  
Vol 3 (2) ◽  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Facial Expression ◽  
Back Propagation ◽  
Particle Swarm ◽  
Back Propagation Neural Network ◽  
Swarm Optimization ◽  
Facial Expression Classification ◽  
Expression Classification

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.

Hysteresis modeling of piezoelectric actuator using particle swarm optimization-based neural network

2020 ◽  
Vol 234 (23) ◽  
pp. 4695-4707
Author(s):  
Quan Zhang ◽  
Xin Shen ◽  
Jianguo Zhao ◽  
Qing Xiao ◽  
Jun Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Moving Average ◽  
Back Propagation ◽  
Particle Swarm ◽  
Piezoelectric Actuators ◽  
Back Propagation Neural Network ◽  
Swarm Optimization ◽  
Network Algorithm ◽  
Neural Network Algorithm

Piezoelectric actuators have been received much attention for the advantages of high precision, no wear and rapid response, etc. However, the intrinsic hysteresis behavior of the piezoelectric materials seriously degraded the output performance of piezoelectric actuators. In this paper, to decrease such nonlinear effects and further improve the output performances of piezoelectric actuators, a modified nonlinear autoregressive moving average with exogenous inputs model, which could describe the rate-dependent hysteresis features of piezoelectric actuators was investigated. In the experiment, the different topologies of the proposed back propagation neural network algorithm were compared and the optimal topology was selected considering both the tracking precision and the structure complexity. The experimental results validated that the modified nonlinear autoregressive moving average with exogenous inputs model featured the hysteresis characteristics description ability with high precision, and the predicted motion matched well with the real trajectory. Then, the initial parameters of the back propagation neural network algorithm were further optimized by particle swarm optimization algorithm. The experimental results also verified that the proposed model based on particle swarm optimization–back propagation neural network algorithm was more accurate than that identified through the conventional back propagation neural network algorithm, and has a better predicting performance.

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