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In this study, the prediction of total power of agricultural machinery is investigated using artificial neural networks (ANN). This paper presents an accurate model by training with value of the total power of agricultural machinery from 1990 to 2003. Values of total power of agricultural machinery from 2004 to 2007 are predicted, predicted results and the ANN are in close agreements with errors less...
The continuous deterioration of ground water quality is one of the important factors that affect development of national economy and society. Based on the DE-BP (Back Propagation-Differential Evolution) neutral network, the predicting model of ground water quality is presented. The precision of the model is checked using the monitoring data in Zhangjiakou area. The comparisons between the predicted...
The continuous decline of ground water level is one of the important factors that affect development of national economy and society. Based on the DE-BP (back propagation-differential evolution) neutral network, the predicting model of ground water level is presented. The precision of the model is checked using the monitoring data in Zhangjiakou area. The comparisons between the predicted results...
This paper is a study of the application of rough set artificial neural networks to the problem of calculating thermal error compensation values for axis positioning on a machine tool. The primary focus is on the development of a rough set approach to reduce a thermal error compensation system which is composed of all of the temperature variables. One modeling of thermal error compensation on machine...
A new algorithm for learning principal curves with explicit formulation is proposed on the basis of artificial neural network (ANN). The algorithm successfully turns an unsupervised learning problem into a supervised one by projecting a data set to the polygonal line learned by existing algorithms of principal curves and identifying the relation between the data points and their corresponding projection...
Considering the time-frequency characteristic of the partial discharge (PD) signals, the kind of improved wavelet neural network is constructed by principle of the temporal-scaling approach, and it is that using temporal-scaling domain of the wavelet basis function being chosen covers that of the partial discharge signals. It is fact that the PD signals are transformed by the wavelet based on the...
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