Temperature drift modeling and compensation of micro-electro-mechanical system gyroscope based on improved support vector machine algorithms

Abstract

This article suggested two methods to compensate for the temperature drift of the micro-electro-mechanical system gyroscopes, which are support vector machine method and C-means support vector machine. The output of X axis which was ranged from −40°C to 60°C based on the micro-electro-mechanical system gyroscope is reduced and analyzed in this article. The results showed the correctness of the two methods. The final results indicate that when the temperature is ranged from −40°C to 60°C, the factor of B is reduced from 0.424 [Formula: see text] to 0.02194 [Formula: see text], and when the temperature is ranged from 60°C to −40°C, the factor of B is reduced from 0.1056 [Formula: see text] to 0.0329 [Formula: see text], and the temperature drift trend and noise characteristics are improved clearly.

Parameter Optimization of Electromechanical System Network Information Processing Based on Support Vector Machine

“…SVM has many advantages over many traditional machine learning methods in solving small sample, nonlinear, and high-dimensional pattern recognition problems, and it can be extended to other machine learning problems such as function fitting. Although there are many problems in statistical learning theory and the SVM method that need to be investigated further, many documents show that they have become a new research hotspot in the field of machine learning after pattern recognition and neural network research, which is beneficial to the advancement of machine learning theory and technology [15]. The structural schematic diagram of SVM is shown in Figure 4 below: The goal requirement of complicated large-scale systems and remote-control systems is system network information processing.…”