摘要
针对传感器输出电信号与物理量之间存在的非线性问题,提出了一种新的曲线拟合方法。该方法通过将改进的粒子群优化算法,混沌搜索法和改进的模糊C-均值算法相结合,对实验数据搜索聚类中心点,然后利用分段线性逼近对传感器输入输出关系进行拟合。介绍了粒子群优化算法和模糊C-均值算法,给出了相关的公式推导过程,对传感器输出电信号与物理量的对应关系进行曲线拟合,最后将该方法应用于电涡流智能传感器。实验结果表明,该方法精度高、可靠性好,具有较强的自适应性和快速性,能够更准确的将电信号转换为物理量。
To solve the nonlinear problems existed between the sensor output signals and physical quantities, this paper proposed a new curve fitting method,which combines the improved particle swarm optimization（PSO） , Chaos search method and modified fuzzy C-means algorithm, to search for the cluster centers from the experimental data, and then uses the piecewise linear approximation to fit the input-output relationship of sensors. The paper firstly introduces the PSO algorithm and fuzzy C-means algorithm, gives the formula derivation, and fits the sensor output signals and physical quantity curve. Finally the proposed method is applied to the eddy current smart sensor. Experimental results show that this method has not only the advantages of high precision, good reliability, strong adaptability and rapidity, but also can accurately convert voltage signal to the physical quantity.
出处
《传感技术学报》
CAS
CSCD
北大核心
2012年第6期789-793,共5页
Chinese Journal of Sensors and Actuators
基金
国家自然科学基金项目(60874033)
高等学校博士学科点专项科研基金项目(2011611811008)
关键词
传感器信号处理
非线性曲线拟合
模糊C-均值聚类
粒子群优化算法
混沌搜索法
signal processing of transducer
nonlinear curves fitting
fuzzy c-means clustering
particle swarmoptimization
chaos search method
