Forecasting Analysis of Nonlinear Complex System in Machine Learning

Abstract

Learning is the process of constructing a model from complex world. And machine learning is concerned with constructing computer programs that automatically improve with experience. Machine learning draws on concepts and results from many fields. Obviously, no matter what we adopt new analytical method or technical means, we must have a distinct recognition of system itself and its complexity, and increase continuously analysis, operation and control level. The forecasting analysis of nonlinear complex system will be discussed carefully in this paper. We are mainly using grey forecasting theory to forecast data sequences, and the usual forecast precision has exceeded 90%. In the symbolic forecast of 2-letters nonlinear complex system, the precision of grey forecasting certainly will decrease as the length of symbolic sequence is increasing. But in this place we have found a generating rule that may realize chaotic synchronization at least in short and medium term, and we can analysis and forecast in this way.