Indicators Forecasting and Empirical Analysis of Regional Economic Development Based on Neural Network

Yu Lin Liu; Jun Qi Yang

doi:10.4028/www.scientific.net/AMM.58-60.1154

Paper Titles

A Method of Multi-Attribute Group Decision-Making Based on LWD and LOWA Operator
p.1130

Global Exponential Stability Analysis for Cohen-Grossberg Neural Networks with Non-Lipschitz Neuron Activations
p.1136

Genetic Algorithm for Parallel-Machine Batching and Scheduling to Minimize Total Weighted Tardiness
p.1142

A Fast Calibration Algorithm Based on Vanishing Point for Scene Camera
p.1148

Indicators Forecasting and Empirical Analysis of Regional Economic Development Based on Neural Network
p.1154

A Characterization of Admissible Linear Estimator of Regression Coefficients in Variance Component Models
p.1162

Quadrilateral Landmark MDS(QLMDS)
p.1168

Research and Design of Transmission Rate Adaptive Control in Public Video Monitoring System
p.1176

Modeling of Web Service-Based Government Decision-Making Support and Collaboration Application Platform - A Case Study of Major Function Oriented Zoning Decision Support
p.1182

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 58-60Indicators Forecasting and Empirical Analysis of...

Indicators Forecasting and Empirical Analysis of Regional Economic Development Based on Neural Network

Abstract:

Based on neural network method and by using the relevant social and economic development history index data, this paper establishes the mathematical model and neural network model that is used to predict future land resource demand, and the network is trained by using data from 1992 to 2005. Accordingly, the trend analysis model, which is to predict and analyses the indicators of population, production value, GDP, etc., is also established, and applied to predict the construction land demand from 2010 to 2020. Here, taking the trend forecasting data of the population, production value and GDP as trained network input, it calculates the future land demand. The simulation result of this method is proved to be satisfactory after comparing it with traditional statistical model forecasting results.

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Periodical:

Applied Mechanics and Materials (Volumes 58-60)

Pages:

1154-1161

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.58-60.1154

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Online since:

June 2011

Authors:

Yu Lin Liu, Jun Qi Yang

Keywords:

Added Value, Construction Land, Forecasting, GDP, Neural Network (NN)

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