Abstract
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.
Highlights
Due to some unsatisfied aspects in manufacturing processes, such as the inconsistent doping concentration, mismatched thermal expansion coefficient among packaging materials, and electronics performance being sensitive to the mutation of the ambient temperature, monocrystalline silicon piezo-resistive pressure sensors suffer from nonlinear input–output characteristics as ambient temperature changes [1].In order to eliminate the crucial disturbance by temperature, a number of techniques were presented
Only provide a really limitedset improvement of the sensor performance; averaged valuescan of training set error and testing error are considered to balance the compensation by utilizing algorithms within the framework of least squares support vector machine (LSSVM), the compensation model can achieve a more results over all runs
A temperature compensation approach within the framework of LSSVM is presented in this research
Summary
Due to some unsatisfied aspects in manufacturing processes, such as the inconsistent doping concentration, mismatched thermal expansion coefficient among packaging materials, and electronics performance being sensitive to the mutation of the ambient temperature, monocrystalline silicon piezo-resistive pressure sensors suffer from nonlinear input–output characteristics as ambient temperature changes [1]. The methods derived from the conventional methods have a lower degree of difficulty when implemented in sensor circuits, artificial intelligence include neural networks [9,10,11,12] and support vector machines [13,14,15] They may encounter some trouble, which would arise from data collection cost with the increasing the conventional methods have a lower degree of difficulty when implemented in sensor circuits, requirement of compensation precision or ill-conditioning problems in solving normal equations. To simplify and accelerate the solving minimum (SRM) principle of SVM that considers both ERM and confidence intervals has more decent process of SVM, Suykens [17] introduced the least squares support vector machine (LSSVM), which learning and generalization ability than neural networks.
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