Machine Failure Prediction Technique Using Recurrent Neural Network Long Short-Term Memory-Particle Swarm Optimization Algorithm

2019 ◽  
pp. 243-252 ◽  
Author(s):  
Noor Adilah Rashid ◽  
Izzatdin Abdul Aziz ◽  
Mohd Hilmi B. Hasan
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Recurrent Neural Network ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Short Term Memory ◽  
Swarm Optimization ◽  
Term Memory ◽  
Machine Failure ◽  
Long Short Term Memory

An improved particle swarm optimization algorithm applied to long short-term memory neural network for ship motion attitude prediction

2019 ◽  
Vol 41 (15) ◽  
pp. 4462-4471 ◽  
Author(s):  
Xiuyan Peng ◽  
Biao Zhang ◽  
Haiguang Zhou
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Short Term Memory ◽  
Particle Swarm ◽  
Ship Motion ◽  
Short Term ◽  
Swarm Optimization ◽  
Term Memory ◽  
Long Short Term Memory

This paper proposes a prediction method of ship motion attitude with high accuracy based on the long short-term memory neural network. The model parameters should be initialized randomly, resulting in critical decreases of the nonlinear learning ability of current parameter optimization methods. Therefore, a multilayer heterogeneous particle swarm optimization is proposed to optimize the parameters of long short-term memory neural network and applied to the prediction of ship motion. In multilayer heterogeneous particle swarm optimization, this paper proposes the concept of attractors, transforms the speed update equation, enhances the information interaction ability between particles, improves the optimization performance of the particle swarm optimization algorithm, and improves its optimization effect on the parameters of the long short-term memory networks. In the simulations, the measured data were used as input to predict the results of the ship motion. The results showed that the proposed method offers higher learning accuracy, faster convergence speed, and better prediction performance for accurate estimation of ship motion attitude than existing methods.

scholarly journals Application of PSO-based LSTM Neural Network for Outpatient Volume Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wenjing Lu ◽  
Wei Jiang ◽  
Na Zhang ◽  
Feng Xue
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Short Term Memory ◽  
Particle Swarm ◽  
Outpatient Management ◽  
Short Term ◽  
Swarm Optimization ◽  
Term Memory

In order to study the construction method of long- and short-term memory neural network model, which is based on particle swarm optimization algorithm and its application in hospital outpatient management, we have selected historical data of outpatient volume of relevant departments in our hospital. Furthermore, we have designed and developed the outpatient volume prediction model, which is based on long- and short-term memory neural network. Additionally, we have used particle swarm optimization algorithm (PSO) to optimize various parameters of long- and short-term memory network and then utilized this optimized model to accurately predict the outpatient volume. Experimental observations, which are collected through the results of monthly outpatient volume prediction, show that Root Mean Square Error (RMSE) of the particle swarm optimized LTMN model on the test set is reduced by 48.5% compared with the unoptimized model. The particle swarm optimization algorithm has efficiently optimized the prediction model, which makes the model better predict the trend of outpatient volume and thus provide decision support for medical staff's outpatient management.

Prediction of air particulate matter in Beijing, China, based on the improved particle swarm optimization algorithm and long short-term memory neural network

2021 ◽  
pp. 1-17
Author(s):  
Shengwei Wang ◽  
Ping Li ◽  
Hao Ji ◽  
Yulin Zhan ◽  
Honghong Li
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Particle Swarm Optimization ◽  
Short Term Memory ◽  
Short Term ◽  
Swarm Optimization ◽  
Term Memory ◽  
Air Particulate ◽  
Long Short Term Memory ◽  
Ipso Algorithm

Intelligent algorithms using deep learning can help learn feature data with nonlinearity and uncertainty, such as time-series particle concentration data. This paper proposes an improved particle swarm optimization (IPSO) algorithm using nonlinear decreasing weights to optimize the hyperparameters, such as the number of hidden layer neurons, learning rate, and maximum number of iterations of the long short-term memory (LSTM) neural network, to predict the time series for air particulate concentration and capture its data dependence. The IPSO algorithm uses nonlinear decreasing weights to make the inertia weights nonlinearly decreasing during the iteration process to improve the convergence speed and capability of finding the global optimization of the PSO. This study addresses the limitations of the traditional method and exhibits accurate predictions. The results of the improved algorithm reveal that the root means square, mean absolute percentage error, and mean absolute error of the IPSO-LSTM model predicted changes in six particle concentrations, which decreased by 1.59% to 5.35%, 0.25% to 3.82%, 7.82% to 13.65%, 0.7% to 3.62%, 0.01% to 3.55%, and 1.06% to 17.21%, respectively, compared with the LSTM and PSO-LSTM models. The IPSO-LSTM prediction model has higher accuracy than the other models, and its accurate prediction model is suitable for regional air quality management and effective control of the adverse effects of air pollution.

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