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

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.

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

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331219860731 ◽

2019 ◽

Vol 41 (15) ◽

pp. 4462-4471 ◽

Cited By ~ 1

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.

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Long Short-Term Memory Neural Intelligent Network Forecast Model Based on Improved Particle Swarm Optimization Algorithm

10.1109/icdsca53499.2021.9650254 ◽

2021 ◽

Author(s):

Yulin Zhu

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Short Term Memory ◽

Forecast Model ◽

Intelligent Network ◽

Short Term ◽

Swarm Optimization ◽

Term Memory ◽

Long Short Term Memory

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Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

Energies ◽

10.3390/en13112873 ◽

2020 ◽

Vol 13 (11) ◽

pp. 2873 ◽

Cited By ~ 1

Author(s):

Dinh Thanh Viet ◽

Vo Van Phuong ◽

Minh Quan Duong ◽

Quoc Tuan Tran

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Wind Power ◽

Optimization Algorithm ◽

Electricity Market ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Short Term ◽

Swarm Optimization

As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to adjust the parameters of the first particle swarm optimization algorithm to enhance the accuracy of the forecasting results. The models were tested with actual data collected from the Tuy Phong wind power plant in Binh Thuan Province, Vietnam. The testing showed improved accuracy and that this model can be widely implemented at other wind farms.

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