Research on Road Roughness Based on NARX Neural Network

In order to solve the problem of road roughness identification, a study on the nonlinear autoregressive with exogenous inputs (NARX) neural network identification method was carried out in the paper. Firstly, a 7-DOF plane model of vehicle vibration system was established to obtain the vertical acceleration and elevation acceleration of the body, which were set as ideal input samples for the neural network. Then, based on the plane model, with common speed, the road roughness was solved as the ideal output sample of the NARX neural network, and the road roughness of B-level and C-level was identified. The results show that the proposed method has ideal identification accuracy and strong antinoise ability. The relative error of C-level road roughness is larger than that of B-level road roughness. The identified road roughness can provide a theoretical basis for analyzing the dynamic response of expressway roads.

Forecasting Soil Moisture In The Soil Under The Caragana Shrubland Using Wavelet Analysis And NARX Neural Network

Purpose: It is important for sustainable use of soil water resources to forecast soil moisture in forestland of water-limited regions. There are some soil moisture models. However, there is not a better method to forecast soil moisture.Methods: The change of soil moisture with time were investigated and the data of soil moisture were divided into a low frequency and a high frequency component using wavelet analysis, and then NARX neural network was used to build model I and model II. For model I, low frequency component was the input variable, and for model II, low frequency component and high frequency component were predicted.Results: the average relative error for model I is 3.5% and for model II is 0.3%. The average relative error of predicted soil moisture in100cm layer using model II is 0.8%, then soil water content in 40 cm and 200 cm soil depth is selected and the forecast errors are 4.9 % and 0.4 %.Using model II to predict soil water is well．Conclusion: Predicting soil water will be important for sustainable use of soil water resource and controlling soil degradation, vegetation decline and crop failure in water limited regions.

Prediction of Ionospheric TEC Based on the NARX Neural Network

Effective prediction of ionospheric total electron content (TEC) is very important for Global Navigation Satellite System (GNSS) positioning and other related applications. This paper proposes an ionospheric TEC prediction method using the nonlinear autoregressive with exogenous input (NARX) neural network, which uses previous TEC data and external time parameter inputs to establish a TEC prediction model. During the years of different solar activities, 12 datasets of 3 stations with different latitudes are used for experiments. Each dataset uses the first 120 days for training and the next 20 days for testing. For each test dataset, a sliding window strategy is adopted in the prediction process, wherein the TEC of future 2 days are predicted by the true TEC values of the previous 2 days. The results show that in the year with active solar activity (2011), the TEC prediction with the NARX network can improve the accuracy by 32.3% and 43.5%, compared with the autoregressive integrated moving average (ARIMA) model and the 2-day predicted TEC product, named C2PG. While in the year with calm solar activity (2017), the prediction accuracy can be improved by 20.7% and 22.7%.

A data calibration method for micro air quality detectors based on a LASSO regression and NARX neural network combined model

Studies have shown that there is a certain correlation between air pollution and various human diseases, especially lung diseases, so it is very meaningful to monitor the concentration of pollutants in the air. Compared with the national air quality monitoring station (national control point), the micro air quality detector has the advantage that it can monitor the concentration of pollutants in real time and grid, but its measurement accuracy needs to be improved. This paper proposes a model combining the least absolute selection and shrinkage operator (LASSO) regression and nonlinear autoregressive models with exogenous inputs (NARX) to calibrate the data measured by the micro air quality detector. Before establishing the LASSO-NARX model, correlation analysis is used to test whether the correlation between the concentration of air pollutants and its influencing factors is significant, and to find out the main factors that affect the concentration of pollutants. Due to the multicollinearity between various influencing factors, LASSO regression is used to further screen the influencing factors and give the quantitative relationship between the pollutant concentration and various influencing factors. In order to improve the prediction accuracy of pollutant concentration, the predicted value of each pollutant concentration in the LASSO regression model and the measurement data of the micro air quality detector are used as input variables, and the LASSO-NARX model is constructed using the NARX neural network. Several indicators such as goodness of fit, root mean square error, mean absolute error and relative mean absolute percent error are used to compare various air quality models. The results show that the prediction results of the LASSO-NARX model are not only better than the LASSO model alone and the NARX model alone, but also better than the commonly used multilayer perceptron and radial basis function neural network. Using this model to calibrate the measurement data of the micro air quality detector can increase the accuracy by 61.3–91.7%.

A Fault Diagnosis and Prognosis Method for Lithium-Ion Batteries Based on a Nonlinear Autoregressive Exogenous Neural Network and Boxplot

The frequent occurrence of electric vehicle fire accidents reveals the safety hazards of batteries. When a battery fails, its symmetry is broken, which results in a rapid degradation of its safety performance and poses a great threat to electric vehicles. Therefore, accurate battery fault diagnoses and prognoses are the key to ensuring the safe and durable operation of electric vehicles. Thus, in this paper, we propose a new fault diagnosis and prognosis method for lithium-ion batteries based on a nonlinear autoregressive exogenous (NARX) neural network and boxplot for the first time. Firstly, experiments are conducted under different temperature conditions to guarantee the diversity of the data of lithium-ion batteries and then to ensure the accuracy of the fault diagnosis and prognosis at different working temperatures. Based on the collected voltage and current data, the NARX neural network is then used to accurately predict the future battery voltage. A boxplot is then used for the battery fault diagnosis and early warning based on the predicted voltage. Finally, the experimental results (in a new dataset) and a comparative study with a back propagation (BP) neural network not only validate the high precision, all-climate applicability, strong robustness and superiority of the proposed NARX model but also verify the fault diagnosis and early warning ability of the boxplot. In summary, the proposed fault diagnosis and prognosis approach is promising in real electric vehicle applications.