Spacecraft Short-Term Fault Prediction Method Based on Echo State Network Time Series

2018 ◽  
Author(s):  
Dawei Wang ◽  
Zhiyun Tan ◽  
Naihai Li ◽  
Yifan Liu ◽  
Xiaojun Han ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Method ◽  
Fault Prediction ◽  
Echo State Network ◽  
Short Term ◽  
Network Time

Preliminary Research of Chaotic Characteristics and Prediction of Short-Term Wind Speed Time Series

2020 ◽  
Vol 30 (12) ◽  
pp. 2050176
Author(s):  
Zhongda Tian
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Time Scales ◽  
Prediction Method ◽  
Test Method ◽  
Echo State Network ◽  
Maximum Lyapunov Exponent ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Different Time Scales

Short-term wind speed prediction has its special significance in wind power industry. However, due to the characteristics of the wind system itself, it is not easy to predict the short-term wind speed accurately. In order to solve the problem, this paper studies the chaotic characteristics and prediction of short-term wind speed time series. The short-term wind speed data at four time scales are collected as the research object. The predictability of short-term wind speed time series is determined by the Hurst exponent. The chaotic characteristics of short-time wind speed at different time scales are analyzed by the 0–1 test method for chaos and the maximum Lyapunov exponent method. The results show that the short-term wind speed time series has chaotic characteristics at different time scales. The phase-space reconstruction technology is introduced; delay time is determined by the C–C method; embedding dimension is obtained by the G–P method. Echo state network is improved to suppress the influence of input noise on prediction performance. At the same time, an improved grey Wolf optimization algorithm is proposed to optimize the parameters of reserve pool of the echo state network. The results of a case study show that, compared with state-of-the-art methods, the proposed prediction method improves the prediction accuracy and reduces the predictive errors.

scholarly journals A Prediction Method Based on Improved Echo State Network for COVID-19 Nonlinear Time Series

2020 ◽  
Vol 08 (12) ◽  
pp. 113-122
Author(s):  
Banteng Liu ◽  
Wei Chen ◽  
Yourong Chen ◽  
Ping Sun ◽  
Heli Jin ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Method ◽  
Nonlinear Time Series ◽  
Echo State Network

Differential Autoregressive of Cycle Prediction Model and its Application

2012 ◽  
Vol 198-199 ◽  
pp. 1315-1320 ◽  
Author(s):  
Cai Lian Luo ◽  
Huan Qi ◽  
Su Qin Sun
Keyword(s):  
Time Series ◽  
Prediction Method ◽  
Stationary Time Series ◽  
Ar Model ◽  
Short Term ◽  
Basic Principles ◽  
Java Programming ◽  
Modeling And Forecasting ◽  
High Prediction ◽  
Short Term Prediction

AR model is widely used which based on stationary time series used for short-term prediction. However, in fact the time series we got is often non-stationary, and there is little literature researching the smooth processing, modeling and forecasting and then restoring the results in system. In view of this, this paper provides a method, that is, differential autoregressive of cycle prediction. First, explain the basic principles and give the calculation steps of smooth processing, modeling and forecasting and restoring the results. Then, applied the prediction method in the short-term forecast of coal arrive of a provincial. Model implementation is based on java programming. We get high prediction accuracy, the system easily integrated, can be widely used, and can achieve rolling forecast.

Chaotic Time Series Adaptive Prediction Based on Volterra Series

2014 ◽  
Vol 945-949 ◽  
pp. 2495-2498 ◽  
Author(s):  
Fang Dai ◽  
Gao Hua Liao
Keyword(s):  
Time Series ◽  
Real Time ◽  
Volterra Series ◽  
Prediction Method ◽  
Fault Prediction ◽  
Statistical Prediction ◽  
Neighborhood Search ◽  
Model Parameters ◽  
Current Time ◽  
Adaptive Prediction

At present, the mine has only realized the real-time monitoring of gas, but not the prediction of gas.There were some limitation of the traditional prediction method, such as modeling subjectivism and statistical prediction. Because it can dynamically adjust the parameters of the model, adaptive prediction method can get the current time according to the prediction error of data and the current time, real-time fault prediction model parameters, this is a very consistent with the prediction method for practical use.This paper presents the gas emission chaos time series method by using volterra series prediction, and on the basis to establish time-series prediction models. The results show that the method not only avoids the phase space reconstruction, but also avoid the points in the neighborhood search, in real-time, with very high efficiency.

scholarly journals Research on Short-Term Traffic Flow Prediction Method Based on Similarity Search of Time Series

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhaosheng Yang ◽  
Qichun Bing ◽  
Ciyun Lin ◽  
Nan Yang ◽  
Duo Mei
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Similarity Search ◽  
Prediction Method ◽  
Flow Data ◽  
Short Term ◽  
Similar Time ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Advanced Traffic Management System

Short-time traffic flow prediction is necessary for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). In order to improve the effect of short-term traffic flow prediction, this paper presents a short-term traffic flow multistep prediction method based on similarity search of time series. Firstly, the landmark model is used to represent time series of traffic flow data. Then the input data of prediction model are determined through searching similar time series. Finally, the echo state networks model is used for traffic flow multistep prediction. The performance of the proposed method is measured with expressway traffic flow data collected from loop detectors in Shanghai, China. The experimental results demonstrate that the proposed method can achieve better multistep prediction performance than conventional methods.

An improved fruit fly algorithm-unscented Kalman filter-echo state network method for time series prediction of the network traffic data with noises

2020 ◽  
Vol 42 (7) ◽  
pp. 1281-1293
Author(s):  
Ying Han ◽  
Yuanwei Jing ◽  
Georgi M Dimirovski
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Network Traffic ◽  
Unscented Kalman Filter ◽  
Prediction Method ◽  
Fruit Fly ◽  
Traffic Prediction ◽  
Echo State Network ◽  
Time Varying ◽  
Traffic Data

With the complexity of the network system rapidly increasing, network traffic prediction has great significance for the safety pre-warning of the network load, network management and control, and improvement of the quality of the network service. In this paper, the time series analysis is used for the network traffic prediction, and a prediction method combined with an optimized unscented Kalman filter (UKF) by an improved fruit fly algorithm (IFOA) and echo state network (ESN) is proposed, which is named by IFOA-UKF-ESN. The researches mainly solve the problem that the prediction accuracy might be greatly affected by the actual network traffic data with unknown and time-varying noises. UKF is used to train the best state vector (formed by spectral radius, scale of the reservoir, scale of the input units and connectivity rate) of ESN; and the proposed IFOA algorithm is proposed to optimize the weights of the predicted state value and the covariance in UKF, which makes UKF have adaptive ability for unknown and time-varying noise. Three actual network traffic data sets with different Gaussian white noise distributions are constructed for experiments, and the experimental results show that the proposed prediction method makes an average improvement by reducing at least 20.60%, 43.23% and 41.85% of RMSE, at least 23.66%, 52.38% and 47.50% of MAE, and at least 23.58%, 52.10% and 47.28% of MAPE, which verify the effectiveness of the proposed method.

scholarly journals Fault Prediction for Nonlinear System Using Sliding ARMA Combined with Online LS-SVR

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Shengchao Su ◽  
Wei Zhang ◽  
Shuguang Zhao
Keyword(s):  
Time Series ◽  
Nonlinear System ◽  
External Disturbance ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Arma Model ◽  
Estimation Algorithm ◽  
Nonlinear Time Series ◽  
Fault Prediction ◽  
Support Vector

A robust online fault prediction method which combines sliding autoregressive moving average (ARMA) modeling with online least squares support vector regression (LS-SVR) compensation is presented for unknown nonlinear system. At first, we design an online LS-SVR algorithm for nonlinear time series prediction. Based on this, a combined time series prediction method is developed for nonlinear system prediction. The sliding ARMA model is used to approximate the nonlinear time series; meanwhile, the online LS-SVR is added to compensate for the nonlinear modeling error with external disturbance. As a result, the one-step-ahead prediction of the nonlinear time series is achieved and it can be extended ton-step-ahead prediction. The result of then-step-ahead prediction is then used to judge the fault based on an abnormity estimation algorithm only using normal data of system. Accordingly, the online fault prediction is implemented with less amount of calculation. Finally, the proposed method is applied to fault prediction of model-unknown fighter F-16. The experimental results show that the method can predict the fault of nonlinear system not only accurately but also quickly.

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