Novel approach for identifying Z-axis drift of RLG based on GA-SVR model

Abstract

This paper describes a novel approach for identifying the Z-axis drift of the ring laser gyroscope (RLG) based on genetic algorithm (GA) and support vector regression (SVR) in the single-axis rotation inertial navigation system (SRINS). GA is used for selecting the optimal parameters of SVR. The latitude error and the temperature variation during the identification stage are adopted as inputs of GA-SVR. The navigation results show that the proposed GA-SVR model can reach an identification accuracy of 0.000 2 ( • )/ h for the Z-axis drift of RLG. Compared with the radial basis function-neural network (RBF-NN) model, the GA-SVR model is more effective in identification of the Z-axis drift of RLG.

Optimal experimental conditions for Welan gum production by support vector regression and adaptive genetic algorithm

“…K ( x i , x j ) is defined as the kernel function. According to Hilbert-Schmidt principle, when kernel function matches Mercer conditions, that is, for any given function g ( x ), if is limited, the value of the kernel is equal to the dot product of two vectors x i and x j in the feature space Φ( x i ) and Φ( x j ), i.e., K ( x i , x j ) = 〈Φ( x i ), Φ( x j )〉 [33]. …”

“…ξ i , are slack variables introduced in order to allow a certain error [ 28 – 32 ]. ξ is also a parameter of the ε -insensitive loss function, where ε is called the tube size [ 33 ]. The greater the value of C is, the greater the penalty for data points beyond the ε deviation, which determines the balance between the degree of smoothness of the function and the number of sample points beyond ε deviation.…”

“…By exploiting the Karush-Kuhn-Tucker (KKT) conditions of the primal optimization problem [ 34 – 36 ], we can get the equation , which means that both of the multipliers and equal to zero, or one of multipliers is zero and is nonzero. The data samples with non-vanishing Lagrange multipliers are called the support vectors inside or outside the ε -insensitive tube [ 33 ].…”

Novel Ensemble Modeling Method for Enhancing Subset Diversity Using Clustering Indicator Vector Based on Stacked Autoencoder

“…Regression, which is frequently used to build mathematical models of complex objects and predict specific output results, has attracted considerable attention in machine learning during the past decades [Vanli, Sayin, Mohaghegh et al (2019)]. In many studies, linear and nonlinear regression and their improved modeling methods based on multivariate statistics and traditional machine learning have been proposed; the modeling methods include ridge regression [Li, Hu, Zhou et al (2018)], least absolute shrinkage and selection operator regression [Xu, Fang, Shen et al (2018); Osborne and Turlach (2011)], partial least squares regression [Lavoie, Muteki and Gosselin (2019); Biancolillo, Naes, Bro et al (2017)], support vector regression (SVR) [Zhang, Gao, Tian et al (2016); Wei, Yu and Long (2014)], and artificial neural network (ANN) [Du and Xu (2017); Martinez-Rego, Fontenla-Romero and Alonso-Betanzos (2012)]. These regression methods have been applied to building mathematical models for various real-life scenarios, such as time series [Safari, Chung and Price (2018); Sarnaglia, Monroy and da Vitoria (2018) ;Sahoo, Jha, Singh et al (2019)] and industry [Xue and Yan (2017); Rato and Reis (2018); Sedghi, Sadeghian and Huang (2017); Khazaee and Ghalehnovi (2018); Gonzaga, Meleiro, Kiang et al (2009)].…”

Temperature Energy Influence Compensation for MEMS Vibration Gyroscope Based on RBF NN-GA-KF Method

“…Chong et al proposed a modeling method based on Elman NN and GA with multiple temperature variable inputs in [23], and the temperature model was established based on temperature, temperature variation rate, and the coupling term. In [24], Wei et al proposed GA and successfully used in the modeling of ring laser Gyro for temperature energy influence drift, and GA was employed to select the optimal parameters of support vector regression. Ding et al proposed a modified RBF NN method of temperature compensation for laser Gyro and improved the accuracy of laser Gyro under different temperatures [25], and this method can quickly and accurately identify the effect of temperature on laser gyro zero bias.…”