| With the continuous and rapid developing of our national economy and the improvement of people's income, the national tourist market is expanding by leaps and bounds. Our national economy is obviously promoted by tourism, which is considered as a dynamic industry, day by day. China will become a giant top-grade nation in tourism in the world. It is a foundational premise for the sustained and healthy development of our national tourism to establish a scientific and applicable forecasting model for tourism demand and give the accurate estimate.How to establish a forecasting model for tourism demand is an important project of tourism study. Researchers, decision makers and employees are all realizing that it is very necessary to forecast tourism demand accurately, but to this day there is not a centralized normal form for which kind of mathematics model can be used. At present commonly used mathematics models is based on statistics, such as time sequence model and regression model (includes linear and nonlinear model), but ANN which has been used extensively in many fields, is seldom used.ANN is an artificial nonlinear dynamic system based on the recognition of cerebra neural network theory. ANN is a theoretic cerebra neural network mathematic model and an information processing system based on imitating cerebra neural net structure. Based on previous samples, it can carry on "learn by itself and model identification. And it is proved to be a good instrument for classification and forecasting in practice application just for it's model identification ability. ANN can discriminate the relativity between training samples precisely, so it is better than traditional statistical method on forecasting function. And that, while the training samples is few and there is random error, ANN is much better than ordinary statistical models. Generally speaking, while the tourism demand statistical data is for a short period time, and tourism demand is disturbed by many unpredictable factors, ANN is a more superior model.In the thesis, based on ANN theory, the author probes into forecasting index selection for tourism demand ANN forecasting model selection establishing procedure andachieving method of ANN forecasting model for tourism demand, and structures forecasting theory for tourism demand.In the thesis, based on time sequence statistical data, applying ANN multi-step prediction and rolling prediction, tourist income and tourist quantities forecasting model is established.The thesis forecasts tourism demand of Qingdao by improved three-layer BP network. In order to make it sure that the data is in same quantity rank, the author adopts normalized method to treat the data imported and exported in advance while training BP net. ANN is trained through fast BP algorithm with variable learning rate that mixed with momentum factor and Levenberg-Marquardt algorithm. These algorithms can improve network's convergence speed. Revised perform function is adopted to improve the popularizing ability of the model.In the study of inbound tourism demand forecasting of Qingdao, 5-8-1 and 3-25-3 ANN structure is respectively adopted to establish tourism foreign exchange income forecasting model and the quantity of inbound tourists forecasting model, then the tourism foreign exchange income and the quantity of inbound tourists in 2003-200& is forecasted. In order to verify the feasibility of ANN, adopting same training sample the author establishes quadratic curve model and index model of tourism foreign exchange income and cubic curve model and index model of total inbound tourist quantity. And with simulation result, the author evaluates the accuracy of all kinds of models above-mentioned through MAPE R and Z. The result indicates simulating accuracy of ANN model is better than any other models above-mentioned.In the thesis, the establishment and achievement of tourism demand ANN forecasting model is with the help of MATLAB. The ANN toolbox of MATLAB constructs many tool functions based on ANN theory. Prediction... |
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