Application of Least Squares-Support Vector Machine in system-level temperature compensation of ring laser gyroscope

Wei, Guo; Li, Geng; Wu, Yan; Long, Xingwu

doi:10.1016/j.measurement.2011.08.032

Cited by 44 publications

(20 citation statements)

References 2 publications

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“…., N, N is the number of data points. Least square support vector machine (LS-SVM), proposed by Suykens and Vandewalle [45], is one of the most famous algorithms developed, which is widely used in many studies such as [46][47][48][49]. LS-SVM is reformulation to the standard SVM.…”

Section: Least Square Support Vector Machine (Ls-svm)mentioning

confidence: 99%

Spark plug fault recognition based on sensor fusion and classifier combination using Dempster–Shafer evidence theory

et al. 2015

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Section: Least Square Support Vector Machine (Ls-svm)mentioning

confidence: 99%

Spark plug fault recognition based on sensor fusion and classifier combination using Dempster–Shafer evidence theory

et al. 2015

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“…The thermal factors that affect the system are self-heating due to system electronics and system external environment temperature. Considering the relationship between temperature and Z-axis drift of RLG is non-linear, we select the temperature variation of Z-axis RLG as another input of GA-SVR [17]. The temperature measurement of Z-axis RLG is conducted by means of fixing a platinum resistance on the RLG itself as a temperature sensor.…”

Section: Experiments Design and Results Analysismentioning

confidence: 99%

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

Wei

Long

2014

J. of Syst. Eng. Electron.

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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.

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“…On the basis of the theory of the statistical learning, SVM is proposed as a general machine learning method to map the input data space using a kernel function to a high-dimensional space (Wei et al 2011). Structural risk minimization principles are used in its implementation that results in superior performance in regression problems as well as classification technique.…”

Section: Modeling Algorithms Support Vector Machine (Svm)mentioning

confidence: 99%

A hybrid error modeling for MEMS IMU in integrated GPS/INS navigation system

Ismail

Abdelkawy

2018

J. Glob. Position. Syst.

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Continuity of accurate navigational data for intelligent transportation applications has been widely provided by utilizing low-cost navigation systems through integrating GPS with micro-electro-mechanical-system (MEMS) inertial sensors. To achieve the required accuracy, augmentation of Kalman filter (KF) with nonlinear error modeling techniques such as fast orthogonal search (FOS) was introduced to enhance the navigational solution by estimating and eliminating a great part of both linear and nonlinear errors of azimuth angle sensed by MEMS gyro. Although this augmented approach enhanced the overall navigational accuracy to some extent, it still suffers from some drawbacks that diverge the system accuracy during GPS long outage periods. These drawbacks stem from the wide-variational behavior and high nonlinearities of the errors in MEMS gyros which make it difficult to depend on the non-adaptive linear error model provided by KF to model the two types of MEMS azimuth errors. In this paper we tried to minimize the effect of uncertainties associated with the KF azimuth prediction during the absence of GPS by introducing a hybrid error model which employs support vector machine (SVM) to model the KF output and FOS, based on autoregressive (AR) concept, to model the nonlinear azimuth errors. The performance of the proposed hybrid SVM-FOS approach is evaluated for GPS/ RISS (Reduced inertial sensor system integrated system) and the results were compared with the conventional KF and augmented KF-FOS approaches.

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Application of Least Squares-Support Vector Machine in system-level temperature compensation of ring laser gyroscope

Cited by 44 publications

References 2 publications

Spark plug fault recognition based on sensor fusion and classifier combination using Dempster–Shafer evidence theory

Spark plug fault recognition based on sensor fusion and classifier combination using Dempster–Shafer evidence theory

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

A hybrid error modeling for MEMS IMU in integrated GPS/INS navigation system

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