Abstract
At present, the commonly used index selection methods for macroeconomic early‐warning research include K‐L information volume, time difference correlation analysis, and horse farm methods. These traditional statistical methods cannot cope with the continuous changes of economic indicators, and due to the existence of statistical errors, these methods are difficult to perform. Therefore, this paper proposes to use a self‐organizing competitive neural network to select early warning indicators. Its self‐learning and adaptive characteristics and fault tolerance overcome the limitations of the above statistical methods. This article proposes a method of selecting macroeconomic early‐warning indicators using self‐organizing competitive neural networks and designs a macroeconomic nonlinear early warning model of self‐organizing competitive neural networks; using fuzzy logic reasoning to introduce economic experts’ experience into macroeconomic early warning analysis, the system has the ability to deal with nonlinear and uncertain problems and realizes the intelligence of the early‐warning process, uses the national macroeconomic indicator data from January 1997 to March 2008 for empirical analysis, and compares the self‐organizing competitive neural network method with the traditional KL information method. From the experimental results, compared with the KL information method, the self‐organizing competitive neural network method selects more comprehensive indicators and has greater advantages in seismic resistance and stability.
Highlights
Macroeconomic early warning is based on realistic or predicted macroeconomic indicators to make judgments on current and future economic operations. e traditional early warning signal system design is to select a set of sensitive indicators that reflect the economic development status and merge them into comprehensive indicators; red, yellow, and green lights are used similar to a set of traffic control signals to control this set of comprehensive indicators. e reflected current and future economic conditions give different signs and signals
How to study the nonlinear problems in the macroeconomic system and how to integrate the experience accumulated by economic experts for many years into the study of future economic trends is an understanding problem that needs to be solved in economic early warning and even macroeconomic research, and it is an understanding of complex systems
When designing an early warning signal system, we must first select a set of sensitive indicators that reflect economic development and use relevant data processing methods to combine multiple indicators into a comprehensive indicator and pass a set of traffic control. e red, yellow, and green signal signs send different signals to the current economic conditions of this group of indicators and comprehensive indicators
Summary
Analysis of Local Macroeconomic Early-Warning Model Based on Competitive Neural Network. Erefore, this paper proposes to use a self-organizing competitive neural network to select early warning indicators. Is article proposes a method of selecting macroeconomic early-warning indicators using self-organizing competitive neural networks and designs a macroeconomic nonlinear early warning model of self-organizing competitive neural networks; using fuzzy logic reasoning to introduce economic experts’ experience into macroeconomic early warning analysis, the system has the ability to deal with nonlinear and uncertain problems and realizes the intelligence of the early-warning process, uses the national macroeconomic indicator data from January 1997 to March 2008 for empirical analysis, and compares the self-organizing competitive neural network method with the traditional KL information method. From the experimental results, compared with the KL information method, the self-organizing competitive neural network method selects more comprehensive indicators and has greater advantages in seismic resistance and stability
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