Performance Of BP Neural Network Research Articles

Modeling and Analysis of Runway Friction Coefficient Prediction Methods Based on Multivariable Coupling

Friction, generated in the contact patch between tires and runway, has long been recognized as a key issue to a safe landing. Moreover, soft sensing technology is also considered to be one of the effective means to improve measurement accuracy and reduce measurement cost of friction coefficient. This article presents a study on the prediction methods of runway friction coefficient based on multivariable coupling. Firstly, the relationships between friction coefficient and tread frictional stress under different working conditions are investigated using finite element analysis; meanwhile, the modeling dataset and test datasets for multivariate coupling are established. Secondly, regression analysis and BP neural network (BPNN) are used to develop prediction models between independent variables (inflation pressure, load, velocity, slip, and frictional stress) and dependent variable (friction coefficient). Finally, the prediction models are evaluated based on internal and external verification. The results demonstrate the superior performance of both prediction models. After further comparison, it is concluded that the performance of BPNN is even better combining the error range and model evaluation indicators including MAE, RMSE, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}^{2}$ </tex-math></inline-formula> .

Prediction of Equatorial Electrojet Based on the Neural Network during Quiet Time

In this study, BP neural network technology was used to predict the variations of Equatorial Electrojet (EEJ) of Indian sector, Peruvian sector and CHAMP satellite during the magnetic quiet pe-riod after 2008. The neural network training data is the corresponding observation data of EEJ during the period of magnetic quiet from 2000 to 2007. The input parameters are day of year, local time, solar zenith angle, solar activity index (<italic>F</italic>_10.7), lunar age and satellite geographic longitude, and the output parameters are EEJ. The EEJ prediction results are statistically analyzed and compared with the observation results. Results show that: BP neural network has good ability in predicting the variations of EEJ in the event, and the prediction results can reflect the important distribution characteristics of EEJ; there is very good correlation between the predicted values and the observed values of EEJ, and the correlation coefficient between the observed values and the predicted values of geomagnetic sta-tions can reach over 85%. In addition, the prediction results of BP neural network model are compared with those of Yamazakis empirical model, and the performance of BP neural network is equivalent with Yamazakis empirical model. The results show that BP neural network has excellent performance in predicting EEJ variations during quiet period, and has a good application prospect.

Activation functions selection for BP neural network model of ground surface roughness

Roughness prediction of ground surfaces is critical in understanding and optimizing the grinding process. However, it is hitherto difficult to predict accurately the ground surface roughness by theoretical and empirical models due to the complexity of grinding process. BP neural network (BPNN), which can be used to establish the relationship between processing parameters and surface roughness, avoids the difficulty of revealing the complex physical mechanism and thus has unique potential in automatic optimization of grinding process in industrial practice. Activation function is one of the most important factors affecting the efficiency and accuracy of BPNN. Nevertheless, it is often selected arbitrarily or at most by trials or tuning. This paper proposes an activation function selection approach in which virtual data generated from the approximate physical model are employed to evaluate the performance of the BPNN in practice application. The results show that with tansig as the activation function of hidden layer and purelin as the activation function of output layer, the BPNN model can obtain the highest learning efficiency. Moreover, when the activation function of hidden layer is sigmoid, whose shape factor is 1–3, and the output layer activation function is purelin, the model can predict more precisely. Finally, the proposed approach is validated by comparing the performance of BPNN obtained from the virtual data and the experimental data. Obtained results showed that the proposed approach is a simple and effective way to determine the activation function of BPNN.

Fiber Bragg grating temperature calibration based on BP neural network

A novel method which applies BP neural network (BPNN) to temperature calibration of fiber Bragg grating (FBG) sensors is proposed and discussed. Processing and analysis of experimental data showed that this method fitted very well the complex relationship between the center wavelength of FBG and temperature which is approximately linear in room temperature whereas nonlinear in low temperature. The maximum absolute error and root mean squared error were respectively 0.9434 °C, 0.2102 °C in fitting and 0.8943 °C, 0.2081 °C in testing which verified the advantage of BPNN fitting compared with the previous polynomial fitting. The forecasting performance of BPNN was also satisfactory. The novel FBG temperature calibration method based on BPNN has considerable application prospect in FBG temperature measurement.