Nonlinear Error Correct of Intelligent Sensor by Using Genetic Algorithms and Cubic Spline Interpolation

Abstract

The new technique in this paper is investigated to correct the nonlinear measurement error of intelligent sensor by use of genetic algorithms and cubic spline interpolation. The principle is using the advantage of both genetic algorithms and cubic spline function to reduce the iteration times and improve measurement precision of intelligent sensor. The way is through selecting genetic algorithms with appropriate design parameter and cubic spline function to build error correction model. The coefficients of the cubic spline function can be figured out by matrix operation. Then use genetic algorithms to fit the first part data, yielding optimization function and optimization coefficients. Simulation result and practice application show this technique is available compared with traditional way such as least-squares method.