On possibilities of using pattern recognition methods to study mathematical models
The possibilities of using pattern recognition methods to study mathematical models with a large number of parameters are discussed. The principal point is to study models by constructing phase and parametric portraits. This allows one to solve problems of predicting the states of the object described by the mathematical model in hand and controlling the object and analyzing and studying problems that follow from the particular content of the model. Examples of three mathematical models are given to illustrate this problem.
This paper expoles an algorithm about dearing surface defects detection by Support Vector Machines and principal component analysis that is the new branch of machine learning,in which the defective area and non-defective area are treated as two different textures and are sampled respectively to be learned,in order to reduce dimension,the image data can be processed by PCA.It is shown that this algorithm works well in defects detection.