Implementing Support Vector Machine And RBF Neural Network On Data Prediction Model

The Artificial Neural Network is an information processing system implemented by computer software. The essence of artificial neural network is an abstracted mathematical model for simulation of human brain functions such as memory, association and reflection. It is the birth and the development of artificial neural networks that provides a new direction for the study on modern nonlinear data analysis. The artificial neural network has a wide range of applications including function approximation or regression analysis, pattern recognition and data classification, clustering, prediction, diagnosis, and process control. The Artificial Neural Network is becoming one research focus on modern machine learning and intelligent system because of its strong adaptive ability, self-organizing capabilities and fault-tolerant ability.The main contents of this article are improvement of Radial Basis Function neural network algorithm and its application in stock price prediction. First, on the basis of summary and comparison of previous studies and achievement, we describe the development of Support Vector Machine (SVM) and RBF neural network, analysis of the theory of SVM and RBF neural network structures, algorithms and applications, then give the mathematical derivation of the main algorithm.