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The establishment financial crisis early warning system has the very vital significance to national economy's construction, the financial crisis is a complex non-linear problem, the present model is based on the linear method, but the neural network is suitable to the processing non-linear problem. This paper improves the BP algorithm with the simulation annealing algorithm, effectively overcomes...
In order to forecast the scale of logistics demand for a new-built airport, economic indicators are used to forecast the scale of logistics demand and the measuring indicator of the scale of logistics demand is studied. The factor analysis and back propagation (BP) artificial neural network theory are applied to set up a model to forecast the scale of the logistics. The application of factor analysis...
Artificial Neural Networks are proposed to model and predict electricity consumption of China. Multilayer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. Energy demand is modeled as a function of economic indicators such as population,...
This paper studies the demand of office building in Shanghai. It defines the demand as the nonlinear function combined with six parameters, including resident population of city, amount of investment on office building construction, the GDP, the rent index of office building, Per capita disposable income and vacancy rate of office buildings. Then, this article uses Logistic Forecasting and Artificial...
Accurate forecasting of some economic indicators such as GDP is very useful. Aiming at the problem of modeling and forecasting of the nonlinear and complex economic system, an improved least square support machine model is proposed in this paper. A multi-scale chaotic search algorithm combined with GA is proposed for the optimum selection of model parameters. Time series data of the indicator to be...
Early-warning system of China's real estate is still in the development of a sound stage, and there are following two main aspects. Firstly, the selection of indicators is to be improved. Secondly, predictive capability of the turning point about the real estate business cycle is to be improved. Based on the above-mentioned problems, the Rough-GA-BP model proposed is applied to the real estate early-warning...
Economic security has been a serious problem which threatens and hampers the sustainable growth of the economy. The paper sets up appraising indicator system of economic security on the basis of PSR model, and researches economic security for 21 coal cities by using BP artificial neural net. As innovation of researching economic security, BP artificial neural net has the better ability in level identification,...
Based on analyzing evolution of production models related to scientific and technical factors, as well as against not well describing quantitative relations between scientific and technical input and output of the models, the present paper has established a model simulating scientific and technological input and output with the back-propagation algorithm of artificial neural networks model, and predicted...
Arable land has been decreasing due to rapid population growth and economic development as well as urban expansion. To obtain a better understanding of controlling land use and to design mechanisms to ensure sustainable land management, an accurate prediction of arable land is a key issue fundamentally. In this study, artificial neural network (ANN) model is applied to estimate the arable land change...
Because the increasing energy demand have an important impact on economic development, the accurate projection of energy consumption is crucial to the energy policy decision. In this study, artificial neural network (ANN) model is used to estimate the energy consumption for Chongqing in China. The projection is implemented using a feed-forward neural network, trained by back-propagation algorithm...
In this paper, using factor analysis to study the sources of China's financial risk, concluded that the major factor of the financial risks is macroeconomic risk, foreign investment risk, banking risk and the stock market risk; using BP artificial neural network model for the establishment of early warning and training and testing the sample data with it, and then prediction the state of the financial...
This paper presents a neuro-based approach for Iran annual gasoline demand forecasting by several socio-economic indicators. In order to analyze the influence of economic and social indicators on the gasoline demand, the gross domestic product (GDP), the population and the total number of vehicles are selected. This approach is structured as a multi-level artificial neural network (ANN) based on supervised...
The Chinese citizen's happiness became a hot issue in recent years, however, few of the scholars researched this issue from the angle of a quality method. Based on this phenomenon, we firstly analyzed the significance of evaluating the Chinese Citizens' happiness, then, we intended to find out the most important elements which may influence our research objects, according to the results of analysis,...
Stock index forecast is not an easy job as it is subject to influence of various factors. Since 1980s, many researchers have used Back Propagation Neural Network BPNN to forecast stock price fluctuations. However, there are some limitations with BPNN. With slow convergent speed and low learning efficiency, BP learning algorithm is easy to get in local minimum and is far from being perfect in stock...
An improved BP Neural Network with additional momentum and adaptive learning is proposed in the paper to predict the growth rate of electricity consumption in China. Matlab7 is used as modeling tool to design the model. Current year GDP growth, electric power consumption growth and growth rate of secondary industry are taken as input variables while next year electric power consumption growth is predicted...
The quantitative relation between output in gross national production (GNP) contributed by science & technology and its input can be constituted. The key methods are to get production distributed by contribution rate of science & technology, to separate input of science & technology from all the inputs for producing GNP respectively using Douglas and Cobb growth model, and then to set...
Traffic demand forecasting is the indispensable process of capacity and resource optimal allocation of comprehensive transport corridor. As such, it is derivate by social and economy activities. The steps of traffic demand forecast of comprehensive transport corridor in this paper is as follows; Step1: Using principal component analysis as a pretreatment to filter out the critical factors that affect...
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