Remaining useful life prediction of ultrasonic motor based on Elman neural network with improved particle swarm optimization

Measurement ◽  
2019 ◽  
Vol 143 ◽  
pp. 27-38 ◽  
Author(s):  
Lin Yang ◽  
Feng Wang ◽  
Jiaojiao Zhang ◽  
Weihao Ren
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Life Prediction ◽  
Particle Swarm ◽  
Ultrasonic Motor ◽  
Remaining Useful Life ◽  
Elman Neural Network ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Useful Life

scholarly journals A Short-Term Forecast Model of foF2 Based on Elman Neural Network

2019 ◽  
Vol 9 (14) ◽  
pp. 2782
Author(s):  
Jieqing Fan ◽  
Chao Liu ◽  
Yajing Lv ◽  
Jing Han ◽  
Jian Wang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Particle Swarm ◽  
Elman Neural Network ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Single Station

The critical frequency foF2 of the ionosphere F2 layer is one of the most important parameters of the ionosphere. Based on the Elman neural network (ENN), this paper constructs a single station forecasting model to predict foF2 one hour ahead. In order to avoid the network falling into local minimum, the model is optimized by the improved particle swarm optimization (IPSO). The input parameters used in the model include local time, seasonal information, solar cycle information and magnetic activity information. Data of the Wuhan Station from 2008 to 2016 were used to train and test the model. The prediction results of foF2 show that the root mean square error (RMSE) of the Elman neural network model is 4.30% lower than that of the back-propagation neural network (BPNN) model. The RMSE is further reduced by 8.92% after using the IPSO to optimize the model. This indicates that the Elman neural network model optimized by the improved particle swarm optimization is superior to the BP neural network and Elman neural network in the forecast of foF2 one hour ahead at Wuhan station.

scholarly journals Position Control of Ultrasonic Motor Using IMC-PID Combined with Tribes Type NN Algorithm

2011 ◽  
Vol 2-3 ◽  
pp. 12-17
Author(s):  
Sheng Lin Mu ◽  
Kanya Tanaka
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Pid Control ◽  
Position Control ◽  
Back Propagation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Ultrasonic Motor ◽  
Swarm Optimization

In this paper, we propose a novel scheme of IMC-PID control combined with a tribes type neural network (NN) for the position control of ultrasonic motor (USM). In this method, the NN controller is employed for tuning the parameter in IMC-PID control. The weights of NN are designed to be updated by the tribes-particle swarm optimization (PSO) algorithm. This method makes it possible to compensate for the characteristic changes and nonlinearity of USM. The parameter-free tribes-PSO requires no information about the USM beforehand; hence its application overcomes the problem of Jacobian estimation in the conventional back propagation (BP) method of NN. The effectiveness of the proposed method is confirmed by experiments.

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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