摘要
针对传感器非线性校正中现有的较为常用的神经网络法的不足和支持向量机参数难确定的问题,提出了一种遗传算法和支持向量机相结合的方法,阐述了支持向量机的非线性校正原理和遗传算法优化支持向量机参数的实现过程,并分别采用BP神经网络法和遗传支持向量机方法对压力传感器进行非线性校正。实验结果表明:BP神经网络法使得传感器的最大相对波动由初始的22.2%降低到1.12%;而遗传支持向量机方法使其降低到0.04%,显著改善了传感器的性能,取得了较好的效果。
In order to overcome the disadvantages of neural networks more commonly used in nonlinear correction of sensors and the problems that the parameters of Support Vector Machine are difficult to determine,a method based on Genetic Algorithm and Support Vector Machine is presented.The nonlinear correction principle of this method is explained.The realization process of Genetic Algorithm optimizing the parameters of Support Vector Machine is introduced.A pressure sensor is adjusted with BP neural network and the method of Genetic Algorithm and Support Vector Machine,respectively.The experiment results show that BP neural network reduces the maximum relative fluctuation from the initial 22.2% to 1.12%.Moreover,the method of Genetic Algorithm and Support Vector Machine reduces the maximum relative fluctuation to 0.04%,therefore it evidently improves the performance of the sensor and achieves better result.
出处
《电子测量与仪器学报》
CSCD
2011年第1期56-60,共5页
Journal of Electronic Measurement and Instrumentation
基金
甘肃省高等学校基本科研业务费资助项目
关键词
遗传算法
支持向量机
压力传感器
非线性校正
genetic algorithm（GA）
support vector machine（SVM）
pressure sensor
nonlinear correction
