Lane Work-Schedule of Toll Station Based on Queuing Theory and PSO-LSTM Model

Wang, Peng; Zhao, Jiandong; Gao, Yuan; Sotelo, Miguel Ángel; Li, Zhixiong

doi:10.1109/access.2020.2992070

Cited by 22 publications

(14 citation statements)

References 14 publications

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“…According to the performance metrics MAPE, RMSE, MAE, and R 2 , the proposed model still achieves the best performance among the four models. Table 3 shows that Wang et al [28] and Shao et al [29] could improve the forecasting results of LSTM. However, the proposed IPSO-LSTM model still has the best performance over all four models.…”

Section: Table 2 Comparison Of the Forecasting Results Corresponding To The Different Evaluation Metricsmentioning

confidence: 99%

“…7 that the IPSO-LSTM forecasting is very close to the true curve. To further verify the forecasting capability of the IPSO-LSTM model, we compare it with the PSO-LSTM and the other improved variable inertia weight methods of Wang et al [28] and Shao et al [29]. Except for the look-back period, the other parameter settings for those models are inherited from the original papers.…”

Section: Table 2 Comparison Of the Forecasting Results Corresponding To The Different Evaluation Metricsmentioning

confidence: 99%

“…As the parameters of LSTM are usually set heuristically, they are unlikely to be optimal. Recent advances indicate that the parameters of LSTM can be updated dynamically using PSO algorithm [28], [29]. However, PSO algorithm often falls into local optimum during iteration.…”

Section: A Ipso-lstm Modelmentioning

confidence: 99%

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A Novel Improved Particle Swarm Optimization With Long-Short Term Memory Hybrid Model for Stock Indices Forecast

Liew

Yang

2021

IEEE Access

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Stock market volatility has a significant impact on many economic and financial activities in the world. Forecasting stock price movement plays an important role in setting an investment strategy or determining the right timing for trading. However, stock price movements are noisy, nonlinear, and chaotic. It is difficult to forecast stock trends for improving return on investment. Here, we proposed a novel improved particle swarm optimization (IPSO) and long-short term memory (LSTM) hybrid model for stock price forecasting. An adaptive mutation factor was used as a parameter for model optimization to avoid premature convergence to a local optimum. Furthermore, we presented a nonlinear approach to improve the inertia weight of the particle swarm optimization and then used the IPSO model to optimize the hyperparameters of LSTM. The experimental results showed that the proposed model outperformed the other related baseline models: support-vector regression, LSTM and PSO-LSTM on the Australian stock market index. These results indicated the proposed model possesses high reliability and good forecasting capability.INDEX TERMS long-short term memory network, time series forecasting, stock indices forecasting, hybrid feature selection, deep learning

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Section: Table 2 Comparison Of the Forecasting Results Corresponding To The Different Evaluation Metricsmentioning

confidence: 99%

Section: Table 2 Comparison Of the Forecasting Results Corresponding To The Different Evaluation Metricsmentioning

confidence: 99%

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A Novel Improved Particle Swarm Optimization With Long-Short Term Memory Hybrid Model for Stock Indices Forecast

Liew

Yang

2021

IEEE Access

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“…GD methods are applied iteratively to LSTM parameters to reduce the cost and to improve accuracy. Wang et al [37] presented a solution for toll stationbased lane work-schedule using toll data. LSTM and PSO algorithm predicts the average length queue of the lane with the three input parameters such as the traffic volume, queue length average, and time taken for service.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient sentiment analysis methodology based on long short-term memory networks

Shobana

Murali

2021

Complex Intell. Syst.

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Sentiment analysis is the process of determining the sentiment polarity (positivity, neutrality or negativity) of the text. As online markets have become more popular over the past decades, online retailers and merchants are asking their buyers to share their opinions about the products they have purchased. As a result, millions of reviews are generated daily, making it difficult to make a good decision about whether a consumer should buy a product. Analyzing these enormous concepts is difficult and time-consuming for product manufacturers. Deep learning is the current research interest in Natural language processing. In the proposed model, Skip-gram architecture is used for better feature extraction of semantic and contextual information of words. LSTM (long short-term memory) is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are optimized by the adaptive particle Swarm Optimization algorithm. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models in different metrics.

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“…At present, because the simplification and validity of PSO, PSO is considered as a promised parameter optimization method [14][15]. However, due to the drawback of premature convergence and local optimal solution, it limits the application in the prediction of PV power domain.…”

Section: State Of the Artmentioning

confidence: 99%

Short-Term PV Power Prediction Based on Optimized VMD and LSTM

Wang

Liu

et al. 2020

IEEE Access

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Because of intermittence and fluctuation of photovoltaic (PV) power, it is difficult to enhance prediction accuracy. To sustain high-efficient operation of power system, this paper proposes a hybrid method to predict the short-term PV power. It consists of components separation of PV power, parameters optimization and reconstruction of prediction result. Firstly, the methods based on the identifying of feature frequency and mutual information maximum are proposed to optimize the mode number and penalty factor of VMD, respectively. The optimized VMD (OVMD) is used to decompose the complicated fluctuation components of PV power into single component. Then, the improved PSO (IPSO) based on non-linear inertia weight of anti-sine function is proposed to optimize the number of hidden layer nodes, learning rate and iteration number of LSTM network. The optimized LSTM is used to predict each single component of OVMD decomposition. Thirdly, the prediction result of each single component is reconstructed to obtain the final PV prediction power. The experiment result indicates that the prediction accuracy of the proposed method (OVMD-IPSO-LSTM) outperformances the other typical methods. By the improvement of the traditional method (VMD and PSO) and the parameter optimization of LSTM, this hybrid method makes a contribution to the prediction of short-term PV power.

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Lane Work-Schedule of Toll Station Based on Queuing Theory and PSO-LSTM Model

Cited by 22 publications

References 14 publications

A Novel Improved Particle Swarm Optimization With Long-Short Term Memory Hybrid Model for Stock Indices Forecast

A Novel Improved Particle Swarm Optimization With Long-Short Term Memory Hybrid Model for Stock Indices Forecast

An efficient sentiment analysis methodology based on long short-term memory networks

Short-Term PV Power Prediction Based on Optimized VMD and LSTM

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