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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...
Macro-economic system is a multi-factor, multi-level, multi-aim typical grey system possessed of indefinite. It's running process is a grey dynamic one composed of many relations of many complex structures and interlaced functions. By using the grey systematic theory model, this paper studies two major problems in China's macro-economic system during the reform and opening up. Firstly, it obtains...
In order to forecast GDP growth much more accurate, a hybrid intelligent system is applied to improve the precision of forecasting, which combines ant colony clustering algorithm (ACCA) and RBF neural network. At first, we can make use of ACCA to cluster the data. And then, this clustered data is used to develop classification rules and train RBF neural network. The effectiveness of our methodology...
In view of the characteristics of our civil aviation, a fuzzy diagonal regression neural networks recurrent forecast model was proposed based on analyzing influential factors of passenger traffic volume. This model deals with the uncertain factors fuzzily and certainty factors using normalization in the front network layer, which solved the problem for inconsistent of importing dimension effectively...
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