传感器动态建模算法的仿真研究

摘要

研究传感器动态优化建模,传感器存在不同程度的相位滞后和惯性等动态特性,当被测参数随时间迅速变化时,动态测试误差较大,严重影响控制精度,对传感器的这种动态性能进行描述是提高检测精度和控制准确度的重要手段.为了提高传感器系统控制精度,减少传感器建模误差,提出一种基于支持向量机的传感器动态建模算法.采用非线性支持向量机对传感器进行动态建模,描述传感器动态、滞后特性,并采用混沌粒子群算法对模型参数进行优化,进一步提高控制精度.仿真结果表明,改进方法能提高传感器系统控制精度,可以很好的应用于现代工业生产过程中,为传感器动态建模提供了一种新的方法.%In order to improve the precision of the control system and reduce the sensor modeling errors, this paper proposed an improved sensor dynamic modeling algorithm. The improved support vector machine was used for the sensor dynamic modeling, which reflects the dynamic characteristics. The chaotic particle swarm algorithm was used to optimize the parameters of the model to improve the control precision. The simulation results show that the method can quickly find the optimal parameters and improve the accuracy of the control system. The model is of good practicality and provides a new method for sensor dynamic modeling.