An Integrated LSTM Prediction Method Based on Multi-scale Trajectory Space

2017 ◽  
Author(s):  
Ming He ◽  
Gongda Qiu ◽  
Jian Shen ◽  
Yuting Cao ◽  
Chamath Dilshan Gunasekara
Keyword(s):  
Prediction Method ◽  
Trajectory Space ◽  
Multi Scale

GRU-corr Neural Network Optimized by Improved PSO Algorithm for Time Series Prediction

2020 ◽  
Vol 29 (07n08) ◽  
pp. 2040010
Author(s):  
Shao-Pei Ji ◽  
Yu-Long Meng ◽  
Liang Yan ◽  
Gui-Shan Dong ◽  
Dong Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Series Data ◽  
Multi Scale ◽  
Network Parameters ◽  
Angle Data

Time series data from real problems have nonlinear, non-smooth, and multi-scale composite characteristics. This paper first proposes a gated recurrent unit-correction (GRU-corr) network model, which adds a correction layer to the GRU neural network. Then, a adaptive staged variation PSO (ASPSO) is proposed. Finally, to overcome the drawbacks of the imprecise selection of the GRU-corr network parameters and obtain the high-precision global optimization of network parameters, weight parameters and the hidden nodes number of GRU-corr is optimized by ASPSO, and a time series prediction model (ASPSO-GRU-corr) is proposed based on the GRU-corr optimized by ASPSO. In the experiment, a comparative analysis of the optimization performance of ASPSO on a benchmark function was performed to verify its validity, and then the ASPSO-GRU-corr model is used to predict the ship motion cross-sway angle data. The results show that, ASPSO has better optimization performance and convergence speed compared with other algorithms, while the ASPSO-GRU-corr has higher generalization performance and lower architecture complexity. The ASPSO-GRU-corr can reveal the intrinsic multi-scale composite features of the time series, which is a reliable nonlinear and non-steady time series prediction method.

scholarly journals A NEPHOGRAM PREDICTION METHOD BASED ON GENERATIVE ADVERSARIAL NETWORK

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 925-930
Author(s):  
Y. Xun ◽  
W. Q. Yu
Keyword(s):  
Loss Function ◽  
Internal Representation ◽  
Prediction Method ◽  
Ground Truth ◽  
Generative Adversarial Network ◽  
Multi Scale ◽  
Adversarial Network ◽  
Basic Characteristics ◽  
Cloud Evolution

Abstract. As one of the important sources of meteorological information, satellite nephogram is playing an increasingly important role in the detection and forecast of disastrous weather. The predictions about the movement and transformation of cloud with certain timeliness can enhance the practicability of satellite nephogram. Based on the generative adversarial network in unsupervised learning, we propose a prediction model of time series nephogram, which construct the internal representation of cloud evolution accurately and realize nephogram prediction for the next several hours. We improve the traditional generative adversarial network by constructing the generator and discriminator used the multi-scale convolution network. After the scale transform process, different scales operate convolutions in parallel and then merge the features. This structure can solve the problem of long-term dependence in the traditional network, and both global and detailed features are considered. Then according to the network structure and practical application, we define a new loss function combined with adversarial loss function to accelerate the convergence of model and sharpen predictions which keeps the effectivity of predictions further. Our method has no need to carry out the stack mathematics calculation and the manual operations, has greatly enhanced the feasibility and the efficiency. The results show that this model can reasonably describe the basic characteristics and evolution trend of cloud cluster, the prediction nephogram has very high similarity to the ground-truth nephogram.

scholarly journals A Lithium-ion Battery RUL Prediction Method Considering the Capacity Regeneration Phenomenon

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2247 ◽  
Author(s):  
Xiaoqiong Pang ◽  
Rui Huang ◽  
Jie Wen ◽  
Yuanhao Shi ◽  
Jianfang Jia ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Health Management ◽  
Prediction Method ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Multi Scale ◽  
Battery Capacity ◽  
Rest Time ◽  
Auto Regressive

Prediction of Remaining Useful Life (RUL) of lithium-ion batteries plays a significant role in battery health management. Battery capacity is often chosen as the Health Indicator (HI) in research on lithium-ion battery RUL prediction. In the rest time of batteries, capacity will produce a certain degree of regeneration phenomenon, which exists in the use of each battery. Therefore, considering the capacity regeneration phenomenon in RUL prediction of lithium-ion batteries is helpful to improve the prediction performance of the model. In this paper, a novel method fusing the wavelet decomposition technology (WDT) and the Nonlinear Auto Regressive neural network (NARNN) model for predicting the RUL of a lithium-ion battery is proposed. Firstly, the multi-scale WDT is used to separate the global degradation and local regeneration of a battery capacity series. Then, the RUL prediction framework based on the NARNN model is constructed for the extracted global degradation and local regeneration. Finally, the two parts of the prediction results are combined to obtain the final RUL prediction result. Experiments show that the proposed method can not only effectively capture the capacity regeneration phenomenon, but also has high prediction accuracy and is less affected by different prediction starting points.

scholarly journals A Novel Integrated Learning Model for Rainfall Prediction CEEMD- FCMSE -Stacking

2022 ◽  
Author(s):  
Xianqi Zhang ◽  
Kai Wang ◽  
Tao Wang
Keyword(s):  
Ensemble Learning ◽  
Goodness Of Fit ◽  
Prediction Method ◽  
Absolute Error ◽  
Prediction Performance ◽  
Monthly Precipitation ◽  
Spatial And Temporal Patterns ◽  
Rainfall Prediction ◽  
Multi Scale ◽  
Precipitation Changes

Abstract Scientific prediction of precipitation changes has important guiding value and significance for revealing regional spatial and temporal patterns of precipitation changes, flood climate prediction, etc. Based on the fact that CEEMD can effectively overcome the interference of modal aliasing and white noise, fine composite multi-scale entropy can reorganize the same FCMSE value to reduce the modal component and improve the computational efficiency, and Stacking ensemble learning can effectively and conveniently improve the fitting effect of machine learning, a rainfall prediction method based on CEEMD-fine composite multi-scale entropy and Stacking ensemble learning is constructed, and it is applied to the prediction of monthly precipitation in the Xixia. The results show that, under the same conditions, the CEEMD-RCMSE-Stacking model reduces the root mean square error by 83.48% and 62.08%, and the mean absolute error by 83.25% and 61.84%, respectively, compared with the single Stacking model and CEEMD-LSTM, while the goodness-of-fit coefficients improve by 15.94% and 2.34%, respectively, which means that the CEEMD-RCMSE-Stacking model has higher prediction performance. The CEEMD-RCMSE-Stacking model has higher prediction performance.

Study on Traffic Prediction Algorithm Based on Multi-Scale Bi-Orthogonal Wavelet

2013 ◽  
Vol 340 ◽  
pp. 722-726
Author(s):  
Yan Li ◽  
Yao Chen
Keyword(s):  
Wavelet Decomposition ◽  
Prediction Method ◽  
Prediction Algorithm ◽  
Traffic Prediction ◽  
Actual Situation ◽  
Prediction Problem ◽  
Network Configuration ◽  
Orthogonal Wavelet ◽  
Multi Scale ◽  
Wavelet Algorithm

The traffic prediction carried out in the communication enterprises is of great significance for the optimization of the network configuration and the improvement of the communication quality. To solve the inaccurate prediction problem under the actual situation, a traffic prediction method based on the bi-orthogonal multi-scale wavelet algorithm is developed. The process of the wavelet decomposition and reconstruction are studied, and the reconstruction results for the different scales wavelet are obtained. Take a set of the special actual samples as the object, the traffic prediction for the future dates is completed, and compared with the actual results. The results show that the relative error between the proposed traffic prediction model and the actual results is less than 10%. The bi-orthogonal multi-scale wavelet algorithm has some advantages as compared with other similar ones, which will provide the important technology means for the traffic prediction forecasting and assessing in the various types of communication enterprises.

scholarly journals A RUL prediction method of equipments based on MSDCNN-LSTM

2021 ◽  
Vol 39 (2) ◽  
pp. 407-413
Author(s):  
Chang Liu ◽  
Wenbai Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Deep Convolutional Neural Network ◽  
Short Term ◽  
Term Memory ◽  
Multi Scale ◽  
Long Short Term Memory

In order to solve the problems of high data dimension and insufficient consideration of time series correlation information, a multi-scale deep convolutional neural network and long-short-term memory (MSDCNN-LSTM) hybrid model is proposed for remaining useful life (RUL) of equipments. First, the sensor data is processed through normalization and sliding time window to obtain input samples; then multi-scale deep convolutional neural network (MSDCNN) is used to capture detailed spatial features, at the same time, time-dependent features are extracted for effective prediction combining with long short-term memory (LSTM). Experiments on simulation dataset of commercial modular aero-propulsion system show that, compared with other state-of-the-art methods, the prediction method proposed in this paper has achieved better RUL prediction results, especially for the prediction of the life of equipment with complex failure modes and operating conditions. The effect is obvious. It can be seen that the prediction method proposed in this paper is feasible and effective.

scholarly journals Thermal Deformation Defect Prediction for Layered Printing Using Convolutional Generative Adversarial Network

2020 ◽  
Vol 10 (19) ◽  
pp. 6860
Author(s):  
Jinghua Xu ◽  
Kang Wang ◽  
Shuyou Zhang ◽  
Guodong Yi ◽  
Jianrong Tan ◽  
...  
Keyword(s):  
Thermal Deformation ◽  
Laser Scanner ◽  
Surface Profile ◽  
Prediction Method ◽  
Small Sample ◽  
Defect Prediction ◽  
Generative Adversarial Network ◽  
Deformation Defect ◽  
Multi Scale ◽  
Adversarial Network

This paper presents a Thermal Deformation defect prediction method for layered printing using Convolutional Generative Adversarial Network (CGAN). Firstly, the original manifold mesh is converted into layered image in Printing Coordinate System (PCS). The trajectory inside layered image with various infill patterns are generated for making comparisons. Inspired by monocular vision and even binocular vision, the mathematical model of thermal defect prediction via infrared thermogram is built via virtual printing of Digital Twins to preset the initial parameters of Artificial Neural Network (ANN). Particularly, the depth convolution is used to extract multi-scale features of layered image. By using transfer learning techniques to identify small sample data, the CGAN is employed to build the nonlinear implicit relations between thermal deformation and multi-scale features. The binocular stereo vision laser scanner is used to determine the actual thermal deformation of the target printed objects. The shape deformation dissimilarity can be succinctly calculated by evaluating the surface profile error via mesh registration between the original source and target mesh model. The proposed method is verified by physical experiments. The experiment proved that the proposed method can deal with the thermal deformation with more optimal parameters, which contributes to performance forward design of irregular complex parts regarding diversified customized requirements.

scholarly journals A multi-scale fusion prediction method for lithium-ion battery capacity based on ensemble empirical mode decomposition and nonlinear autoregressive neural networks

2019 ◽  
Vol 15 (3) ◽  
pp. 155014771983963 ◽  
Author(s):  
Pei Wang ◽  
Xue Dan ◽  
Yong Yang
Keyword(s):  
Neural Networks ◽  
Lithium Ion Battery ◽  
Empirical Mode Decomposition ◽  
Prediction Method ◽  
Lithium Ion ◽  
Ensemble Empirical Mode Decomposition ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Battery Capacity ◽  
Nonlinear Autoregressive

Lithium-ion battery has been widely used in various fields due to its excellent performance. How to accurately predict its current capacity throughout a battery full lifetime has been a key technology for power system management, assurance, and predictive maintenance. In order to overcome low precision problem in long-term prediction for lithium-ion battery capacity, this article proposes a multi-scale fusion prediction method based on ensemble empirical mode decomposition and nonlinear autoregressive models neural networks. The proposed method uses ensemble empirical mode decomposition to decompose the battery capacity measurement sequence to generate multiple intrinsic mode function components on different scales. Then, each component is predicted by nonlinear autoregressive neural networks; finally, the prediction results of each component are reconstructed to obtain the final battery capacity prediction sequence. Experimental results show that the proposed method has higher prediction accuracy and signal adaptability than single nonlinear autoregressive neural networks.

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