A new approach to abrupt change detection based on Fisher information

Abstract

The dynamics structure of many complex system there often happens a suddenly change due to the effect of outside force in nature. This abrupt change is closely related to human life. In order to make an accurate prediction of abrupt change and take corresponding measures, abrupt change should be detected effectively. Fisher information (FI) can keenly catch and characterize a small change in probability density distribution (PDD) of a system variable. While there is a change in dynamic structure of a system, the PDD of the system variable will have some changes correspondingly. In view of this, in this paper we describe in detail that FI is used to detect and recognize the abrupt change of system dynamics structure, and it is a new method to solve the detection of abrupt change in dynamic structure of a system. First of all, this method is used to present the ability to detect abrupt change lying in ideal datasets of different types of ideal signals. Next, this method is used to analyze daily datasets of ground climate from national meteorological information center of China meteorological administration between 1960 and 2008 in Lanzhou meteorological observation station in Northwest area. The results show that the daily datasets are consistent with historical records, which further verifies that the method is effective and practical.