Research on Genetic Algorithm and Data Information based on Combined Framework for Nonlinear Functions Optimization

In recent years, piecewise linear change has become an attractive tools, used for all kinds of complicated nonlinear system. Piecewise linear individual function to provide the platform segmental affine nonlinear system contains a large amount of counter approximate nonlinear function value. Even if section of linearization method widely used the best approximation of the nonlinear function of continuous time a minimum number of piecewise functions did not mention liveried with appropriate literature. This paper presents a method of optimization based on clustering evolution get optimal piecewise linear approximation of a class of nonlinear function. The technology is based on the balance between the approximate precision and simplified, and improves the approximate Linear A minimum number of department. The technology has been successfully applied in some common nonlinear function.