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This paper proposed a new hybrid forecasting model for the prediction of ozone concentrations in semi-arid area. It is based on chaotic, particle swarm optimization algorithm (CPSO) and back propagation (BP) neural network, called CPSO-BP neural network. The results show that the proposed hybrid model is superior to both the BP neural network and the regression model being tested. The hybrid model...
In this paper the wind speed forecasting in a wind farm, applying the algorithm of support vector regression (SVR) to the mean 10-minute time series is presented. By comparing its performance with an back propagation neural network model through simulation results, we could find following facts: firstly, both algorithms are applicable for prediction the wind speed time series in future; secondly,...
Equal Salt Deposit Density (ESDD) is a main factor to classify contamination severity and draw pollution distribution map. To cope with the problems existing in the ESDD predicting by multivariate linear regression (MLR), back propagation (BP) neural network and least squares support vector machines (LSSVM), a nonlinear combination forecasting model based on wavelet neural network (WNN) for ESDD is...
Applying the original experimental data of series 60 ship models, four-layer back propagation neural network is founded. Test samples and interpolated samples are randomly selected as input vectors. The worse of the maximum relative error, the average relative error and the correlation coefficient between the outputs and the goals, their regression lines and the performance curves plotted by the neural...
Accurate springback prediction and control is essential for sheet metal forming. In this paper, back propagation (BP) neural network and genetic algorithm (GA) was introduced to predict springback of complex sheet metal forming parts. GA was used to optimize the weights of BP neural network and the results were compared with those of traditional BP neural network and regression model. The comparison...
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