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

Peng, Xiuyan; Zhang, Biao; Zhou, Haiguang

doi:10.1177/0142331219860731

Cited by 22 publications

(5 citation statements)

References 17 publications

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“…The connection pattern between model neurons is reflected in the data processing method used to extract useful information from existing data. Neural networks are effective for problems that are difficult to adapt to the calculation formula [18][19]…”

Section: B Optimization Of Lstmmentioning

confidence: 99%

Retracted: Mathematical Model of Yield Forecast Based on Long and Short-Term Memory Image Neural Network

2024

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Yield prediction has always been the focus of stock investors' attention in stock investment. This research uses the virtual stock scene constructed by VR technology to explore the mathematical model of return rate prediction. First, neural nodes, activation functions and output functions are designed under the LSTM framework, and a recurrent neural network that can handle long-term sequences is constructed. Use VR technology to simulate stock data and extract key frames from the image sequence. Aiming at the timing of key curve nodes, this study uses a long and short-term memory neural network that is good at processing time series data to classify curve trends, and realizes the identification of abnormal fluctuations in virtual scenes of stock images. Then, according to the existing probability control asset allocation theory, the index of investment allocation is adjusted from the entire time series to a dynamic time series. According to the stock price decomposition of EEMD, the EEMD-LSTM model is constructed to predict the stock market price. Use EEMD to decompose the original sequence to obtain IMF and trend items containing various timescale characteristics. Then, the LSTM neural network is used to predict and analyze each rate of return, and the analysis software Python is used to process and count the predicted results. At the same time, retrospective restrictions are regarded as riskier assets. Finally, the investment is made under conditions that do not allow shorting in the VR simulation scenario. The accuracy of the calculation is more than 70% except for the Shanghai and Shenzhen 300 Index reaching 66%. The research results show that in the case of different stock price forecasts, the prediction ability of LSTM neural network has been greatly improved compared with that of support vector machine, which fully reflects the advantages of LSTM in time series prediction.

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Section: B Optimization Of Lstmmentioning

confidence: 99%

Retracted: Mathematical Model of Yield Forecast Based on Long and Short-Term Memory Image Neural Network

2024

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“…, x t are the input sequence, h t and ← h t are the forward and reverse outputs calculated at each moment respectively, and then the forward and reverse outputs are calculated to obtain the final output y t . Taking the forward output h t at time t as an example, the calculation formulas of forward and backward directions are consistent with LSTM, that is, through ''(1)'' to '' (8) o t can be calculated respectively. The final output y t at time t is:…”

Section: B Bilstm Neural Networkmentioning

confidence: 99%

“…Compared with the single prediction method, the combined forecasting method is hoped to make a more accurate prediction. Peng et al applied a particle swarm optimization algorithm to long short-term memory (LSTM) neural network [8], and many other scholars also applied the combined forecasting method to prediction problems [9]- [11]. The combined forecasting method is the hot spot of the current forecasting research.…”

Section: Introductionmentioning

confidence: 99%

Ship Motion Attitude Prediction Based on an Adaptive Dynamic Particle Swarm Optimization Algorithm and Bidirectional LSTM Neural Network

Zhang

Tan

2020

IEEE Access

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A new neural network prediction model is proposed for predicting ship motion attitude with high accuracy. This prediction model is based on an adaptive dynamic particle swarm optimization algorithm (ADPSO) and bidirectional long short-term memory (BiLSTM) neural network, which is to optimize the hyperparameters of BiLSTM neural network by the proposed ADPSO algorithm. The ADPSO algorithm introduces dynamic search space strategy into the classical particle swarm optimization algorithm and adjusts the learning factor adaptively to balance the global and local search ability, so as to improve the optimization performance and improve its optimization effect in BiLSTM parameter optimization process. The results show that the model can obtain higher prediction accuracy and faster convergence speed, and has better prediction performance in the prediction of ship motion attitude. INDEX TERMS Ship motion attitude, BiLSTM neural network, ADPSO algorithm, prediction accuracy.

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“…Lin et al [9] proposed a novel approach for plan-path prediction based on the relative motion between positions by mining historical flight trajectories. Peng et al [10] presented an improved particle swarm optimization algorithm applied to a long short-term memory neural network for the prediction of ship motion attitude. Xiao et al [11] developed a vehicle positioning approach by employing a support vector machine for regression (SVR) to achieve accurate and reliable vehicle position and trajectory prediction based on the GPS receiver and an on-board diagnostics reader.…”

Section: Introductionmentioning

confidence: 99%

Surfacing Positioning Point Prediction of Underwater Glider with a New Combination Model

Zhang

Niu

Wan

et al. 2023

JMSE

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Combination prediction models have gained great development in the area of information science, and are widely applied in engineering fields. The underwater glider (UG) is a new type of unmanned vehicle used in ocean observation for the advantages of long endurance, low noise, etc. However, due to its lower speed relative to the ocean current, the surfacing positioning point (SPP) of an UG often drifts greatly away from the preset waypoint. Therefore, this paper proposes a new combination model for predicting the SPP at different time scales. First, the kinematic model and working flow of the Petrel-L glider is analyzed. Then, this paper introduces the principles of a newly proposed combination model which integrates single prediction models with optimal weight. Afterwards, to make an accurate prediction, ocean current data are interpolated and averaged according to the diving depth of UGs as an external influencing factor. Meanwhile, with sea trial data collected in the northern South China Sea by Petrel-L, which had a total range of 4230.5 km, SPPs are predicted using single prediction models at different time scales, and the combination weights are derived with a novel simulated annealing optimized Frank–Wolfe method. Finally, the evaluated results demonstrate that the MAE and MSE are 966 m and 969 m, which proves that the single models achieved good performance under specified situations, and the combination model performed better at full scale because it integrates the advantages of the single models. Furthermore, the predicted SPPs will be helpful in the dead reckoning of the UG, and the proposed new combination method could extend into other fields for prediction.

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

Cited by 22 publications

References 17 publications

Retracted: Mathematical Model of Yield Forecast Based on Long and Short-Term Memory Image Neural Network

Retracted: Mathematical Model of Yield Forecast Based on Long and Short-Term Memory Image Neural Network

Ship Motion Attitude Prediction Based on an Adaptive Dynamic Particle Swarm Optimization Algorithm and Bidirectional LSTM Neural Network

Surfacing Positioning Point Prediction of Underwater Glider with a New Combination Model

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