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This paper presents an application of probabilistic neural networks (PNN) to integrated analysis multi-mineral anomalies caused by geological information (geology, geophysics, geochemistry, and remote sensing) and to map the 1:25000 scale potential for Molybdenum polymetallic Pb-Zn-Ag mine targets with in Luanchuan region, Hennan Province. On the one hand, according to geological anomaly theory, the...
With the rapid growth of the national investment in sewage treatment industry in China, more and more sewage treatment plants have been set up. Therefore, investment efficiency of the sewage treatment plants is of great significance. Since the traditional CCR model does not take into account "slack" of input elements, and it's unable to solve the problem of ranking efficient DMUs, this paper...
Solar radiation knowledge is important for the solar energy conversion and utilization. In this work, least squares-support vector machine (LS-SVM) algorithms were applied to estimate the yearly and monthly average daily global solar radiation in China using the ordinary meteorological data and geographic parameters. The monthly climatic data from 101 radiation measurement stations were divided into...
A displacement model using the back propagation algorithm of artificial neural networks (BP-ANN) optimized with a genetic algorithm (GA) was presented on the example of an arch-type dam in China. The settlement displacement analysis for a single point located on the dam was performed. The analysis consists of three stages: principal component analysis (PCA), BP-ANN modelling, and deformation forecast...
In the analysis of predicting power load forecasting based on least squares neural network, the instability of the time series could lead to decrease of prediction accuracy. On the other hand,neural network and chaos theories parameters must be carefully predetermined in establishing an efficient model. In order to solve the problems mentioned above, in this paper, the neural network and chaos theory...
In this paper, a novel artificial neural network ensemble rainfall forecasting model is proposed for rainfall forecasting based on K-nearest neighbor nonparametric estimation of regression. In this model, original data set are partitioned into some different training subsets via Bagging technology. Then different ANN algorithms and different network architecture generate diverse individual neural...
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