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Aiming at the disadvantages of prediction model of single BP neural network, a prediction model was presented by combining AdaBoost algorithm and BP neural network for improving the forecasting accuracy of single BP neural network. A new updating method is proposed for the characters of ensemble BP neural network based on AdaBoost. The new method can update the model effectively and overcome the disadvantage...
Nonparametric Linear Regression and Artificial Neural Network models have been developed based on different perspectives and assumptions. In this paper a survey is made to compare the predictive performances of the nonparametric models of closing prices of Stock Index data, where the data is non normal. Comparative studies with the existing statistical prediction models indicate that the proposed...
This paper uses GMDH method to establish a prediction model to forecast the output value of transport & storage of Guangdong in China, since the original samples of the output value of transport & storage are less enough to be used with the traditional methods. Compared with traditional linear regression and artificial neural network, the predicted results show that GMDH method is an effective...
At present, research on nonlinear network flows of mobile short message is one hotspot in mobile communications fields. Nonlinear network flows of mobile short message have such essential features varying with time as periodicity, regularity, correlation, randomicity, occasionality. The traditional methods based on linear models are successful relatively in making irregular flow series become more...
Through literature consulting and correlation analysis, this paper selects seven important indicators which have close relation with CPI. Then on the basis of neural network theory and MATLAB neural network toolbox, this paper constructs a CPI prediction model. Finally, by using test samples to make a emulate experiment, the simulation result indicates that the model is feasible and effectual.
A new model is introduced in this paper to construct the input-output relation in the prediction and control problem of non-analytic systems. The historical input-output data of general system is de-noised with wavelet transformation and SVM, and the input-output variables which can reflect the features of the system are determined with correlation analysis and sensitivity analysis. With the historical...
Wind power prediction is important to the operation of power system with comparatively large mount of wind power. It can relieve or avoid the disadvantageous impact of wind farm on power systems. Because the traditional neural network may fall into local convergence, so it will be effective to improve the training algorithm to improve its convergence and accuracy of prediction. In this paper, a model...
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...
River temperature prediction is an important project in the environmental impact assessments. Based on river temperature data of Yichang hydrological station in the middle reach of the Yangtze River, BP neural network model based on particle swarm optimization (PSO) was applied to predict river temperature of the Yangtze River. PSO was used to optimize the initial weights of nodes in BP neural network...
Time series forecasting is an important aspect of dynamic data analysis and processing, in science, economics, engineering and many other applications there exists using the historical data to predict the problem of the future, and is one considerable practical value of applied research. Time series forecasting is an interdisciplinary study field, this paper is under the guidance of the introduction...
With the moving dune in sandy land of Northwest Liaoning province as the research object, its water variation in soil was simulated and studied based on a BP Neural Network model. With principal meteorologic factors that affect soil water, such as precipitation and evaporation, as the input variables and the water content in soil as the output variable, a soil-water prediction model based on BP NN...
In recent years, artificial neural networks (ANN) have emerged as a novel identification technique for the forecasting of hydrological time series. However, they represent their knowledge in terms of a weight matrix that is not accessible to human understanding at present. The purpose of this study is to develop a flow prediction method, based on the genetic programming (GP), which is an evolutionary...
In this study, a Al-agent-based trapezoidal fuzzy ensemble forecasting model is proposed for crude oil price prediction. In the proposed ensemble model, some single AI models are first used as predictors for crude oil price prediction. Then these single prediction results produced by the single Al-based predictors are fuzzified into some fuzzy prediction representations. Subsequently, these fuzzified...
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