Study On Intelligent Nonlinear System Modeling Based On Approching Element

Now nonlinear-system modeling method based input and output are the hot points of system modeling, which consists of many new methods, such as regression analysis, artificial intelligence, pattern recognition, machine learning and neural networks. Support Vector Machine (SVM) is a new kind of machine learning algorithm based on the statistical learning theory. Because of its standout learning capability, the algorithm has been one of the hot points in international machine learning research. Based on the idea of structural risk minimization, it has been proved that the advantages of SVM include good generalization performance, global optima property and small time complexity. Due to its perfect theoretical properties and good empirical results, SVM now attracts more attentions from researchers, which involves any practical problems such as classification and regression estimation. In this paper, we study the prediction modeling methods based on support vector regression (SVR) theory.A kind of intelligent nonlinear system model is constructed based on approaching element. Without knowing the mechanism of nonlinear system we can construct the model of nonlinear system just studying on kinds of outer data of the object. The intelligent nonlinear system modeling studies rules through the observational data, and then predicts the coming data or the data that can't be observed through the constructed system model upon the rules. As in the real world there are a lot of objects with unknown inherent mechanism, this method of modeling based on data has a universal application.