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Research Into The Application Of Artificial Neural Network To Macroeconomy Prediction

Posted on:2006-06-04Degree:MasterType:Thesis
Country:ChinaCandidate:H ChenFull Text:PDF
GTID:2166360155972217Subject:Control Engineering
Abstract/Summary:PDF Full Text Request
With the further development of 'Golden Finance Project', the IT construction of grass roots finance administration institutions is becoming more and more prosperous. In the period of the tenth five-year planning, half of the rural finance administrative institutions will complete the construction of their management information system. While developing the informationizaton, it becomes particularly significant to manage the data of the regional finance system, analyze the data in terms of macroeconomy prediction, and provide upper administrative organization with information for whole control, prediction and system-based analysis. Nevertheless, most of the system-based prediction and analysis software that could be available in the market place are made overseas and fairly expensive. It is not cost effective if the software is employed by regional finance administrative organizations. In terms of the local financial authorities with financial disadvantages, the development and research of financial information have been put forward to meet the needs at different levels. Therefore, an alert mechanism has been set up for the macroeconomy of the government. Macroeconomy system represents the comprehensive economic status in a country or an area. Finance, as one of the most important parts of national economy, is a crucial macro regulation and control means which is a very complicated system requiring positive and effective regulation. And prediction is needed to be made at first, which means making the judgment for the trend of the matter through theory and certain methods according to historical records and current conditions. Among the current prediction methods, the most common statistics methods are time-sequencing and return predictions. And macroeconomy is a nonlinear system, whose situation keeps changing. Besides, additional interference factors have direct effects on the operation of macroeconomy systems, greatly influencing the prediction results. Because the historic data needed for macroeconomy models are not stable, not accurate and not complete, it is necessary to solve such problems by using the traditional prediction methods. Therefore, artificial Neural networks are applied to prediction. Artificial Neural network is a nonlinear dynamic system, which can realize the reflection of nonlinear relations among constants within the range of any accuracy, capable of solving nonlinear problems, learning network technology, integrating systems. It is able to satisfy the needs for the prediction of macroeconomy index, enabling the system to solve the nonlinear and unfixed problems, and hence improving the accuracy of prediction. As macroeconomy system is complicated, it is necessary to analyze the characteristics of macroeconomy system and study the index system of macroeconomy under the guidance of economic theory. Based on the nonlinear and unfixed features of macroeconomy system, the combination of descriptive method and accuracy is adopted, which is the major method to explore the accuracy prediction of macroeconomy. The theory on artificial neural network is applied to construct the modeling system of macroeconomy prediction. The modeling method to combine the multi-item integration and BP Neural network has been put forword, which improves the accuracy of the system prediction, realizes the index prediction of regional macroeconomy and describes the developing trend of macroeconomy. Under the guideline of economic theories, the preliminary probe and preparation have been done for Macroeconomy prediction and its system's prediction modeling in the dissertation in terms of theories, approaches and algorithm, providing a new concept and method to prediction Macroeconomy prediction.
Keywords/Search Tags:Macroeconomy, Macoeconomy Prediction, Artificial Neural Network Prediction, BP Algorithm
PDF Full Text Request
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