Li Ji, Hu Guoqing, Zhou Yonghong, Zou Chong, Peng Wei, Alam Sm Jahangir
Department of Mechanical and Electrical Engineering, School of Aerospace Engineering, Xiamen University, Xiamen 361005, China.
Department of Mechatronics Engineering, School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou 510640, China.
Sensors (Basel). 2016 Oct 14;16(10):1707. doi: 10.3390/s16101707.
A piezo-resistive pressure sensor is made of silicon, the nature of which is considerably influenced by ambient temperature. The effect of temperature should be eliminated during the working period in expectation of linear output. To deal with this issue, an approach consists of a hybrid kernel Least Squares Support Vector Machine (LSSVM) optimized by a chaotic ions motion algorithm presented. To achieve the learning and generalization for excellent performance, a hybrid kernel function, constructed by a local kernel as Radial Basis Function (RBF) kernel, and a global kernel as polynomial kernel is incorporated into the Least Squares Support Vector Machine. The chaotic ions motion algorithm is introduced to find the best hyper-parameters of the Least Squares Support Vector Machine. The temperature data from a calibration experiment is conducted to validate the proposed method. With attention on algorithm robustness and engineering applications, the compensation result shows the proposed scheme outperforms other compared methods on several performance measures as maximum absolute relative error, minimum absolute relative error mean and variance of the averaged value on fifty runs. Furthermore, the proposed temperature compensation approach lays a foundation for more extensive research.
压阻式压力传感器由硅制成，其性能受环境温度影响较大。为实现线性输出，工作期间应消除温度影响。针对此问题，提出一种由混沌离子运动算法优化的混合核最小二乘支持向量机（LSSVM）方法。为实现良好性能的学习与泛化，将由局部核径向基函数（RBF）核和全局核多项式核构成的混合核函数引入最小二乘支持向量机。引入混沌离子运动算法来寻找最小二乘支持向量机的最佳超参数。通过校准实验的温度数据对所提方法进行验证。考虑到算法的鲁棒性和工程应用，补偿结果表明所提方案在最大绝对相对误差、最小绝对相对误差、五十次运行平均值的均值和方差等多项性能指标上优于其他对比方法。此外，所提温度补偿方法为更广泛的研究奠定了基础。
