Gyro Error Compensation in Optoelectronic Platform Based on a Hybrid ARIMA-Elman Model

Abstract

As an important angle sensor of the opto-electric platform, gyro output accuracy plays a vital role in the stabilization and track accuracy of the whole system. It is known that the generally used fixed-bandwidth filters, single neural network models, or linear models cannot compensate for gyro error well, and so they cannot meet engineering needs satisfactorily. In this paper, a novel hybrid ARIMA-Elman model is proposed. For the reason that it can fully combine the strong linear approximation capability of the ARIMA model and the superior nonlinear compensation capability of a neural network, the proposed model is suitable for handling gyro error, especially for its non-stationary random component. Then, to solve the problem that the parameters of ARIMA model and the initial weights of the Elman neural network are difficult to determine, a differential algorithm is initially utilized for parameter selection. Compared with other commonly used optimization algorithms (e.g., the traditional least-squares identification method and the genetic algorithm method), the intelligence differential algorithm can overcome the shortcomings of premature convergence and has higher optimization speed and accuracy. In addition, the drift error is obtained based on the technique of lift-wavelet separation and reconstruction, and, in order to weaken the randomness of the data sequence, an ashing operation and Jarque-Bear test have been added to the handle process. In this study, actual gyro data is collected and the experimental results show that the proposed method has higher compensation accuracy and faster network convergence, when compared with other commonly used error-compensation methods. Finally, the hybrid method is used to compensate for gyro error collected in other states. The test results illustrate that the proposed algorithm can effectively improve error compensation accuracy, and has good generalization performance.

Research on the predictive effect of a combined model of ARIMA and neural networks on human brucellosis in Shanxi Province, China: a time series predictive analysis

“…The research showed that the combined model had significantly higher prediction accuracy than the single model [30]. Currently, a combined model based on ARIMA and ERNN (ARIMA-ERNN) is mainly applied to air pollution prediction [31], spot price forecasting [32], error compensation [33] and other fields. Nevertheless, there have been no reports in the use of the combined model to predict the epidemic trend of human brucellosis.…”

Deep Learning-Based Channel Prediction for Edge Computing Networks Toward Intelligent Connected Vehicles

“…ARIMA is a category of time series prediction model that figures out the inherent relationship among the data. However, one limitation of ARIMA is that it cannot capture the feature of a rapidly changing data sequence [16]. As one class of machine learning methods, SVR has disadvantages in high algorithmic complexity and the selection of the kernel function parameters.…”