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We established an experimental region located at Wang Ye Dian forest farm in Chifeng City, Inner Mongolia, China. Within this region, the catastrophe theory model applied for 6 common species single wood volume measurement and calculation to establish a growth model for trees. The experiment was carried out through the actual measurement of the tree height (H), DBH (D), volume (V), diameter (D0) as...
Based on the real data of a Chinese commercial bank's credit card, in this paper, we classify the credit card customers into four classifications by K-means. Then we built forecasting models separately based on four data mining methods such as C5.0, neural network, chi-squared automatic interaction detector, and classification and regression tree according to the background information of the credit...
Microbially assisted recovery of copper from low-grade chalcopyrite has been reported to be a very difficult process, conventional hydrometallurgical methods were limited by many parameters. This study focus on the design and the training of a Multi-Layer Perceptron classifier for the optimized preparation conditions for bioleaching of chalcopyrite. The proposed approach uses the heuristic Backpropagation...
CO2 capture processes by carbonation-calcination cycles of CaO/CaCO3 were limited by the carbonation conversion and sorbents reutilization with the number of carbonation/calcinations cycles. In order to optimizing the CaO/CaCO3 cycles, BP neural network model and PID temperature control system were established based on the simulation of the process parameters and dynamic characteristics. The carbonization/calcination...
GM model is widely applied in many fields, in this paper, a refined GM(l,l)-improved genetic algorithm (GM(1,1)- IGA) is put forward to solve short-term load forecasting (STLF) problems in power system. Traditional GM(1,1) forecasting model is not accurate and the value of parameter a is constant, while the proposed algorithm could overcome these disadvantages. GM(1,1)-IGA established a function Z(c)...
According to Traditional Grey Model (GM(1, 1)) is not accurate and the value of parameter is constant, in order to overcome these disadvantages, this paper put forward an improved genetic algorithm-GM(1, 1) (IGA-GM (1, 1)) to solve the problem of short-term load forecasting (STLF) in power system. The proposed algorithm not only improved the original series but also constructed optimal grey model...
A mathematical model known as grey model GM(1,1) has been employed successfully in the forecasting of power load system. Because traditional GM (1, 1) forecasting model is not accurate and the value of parameter alpha is constant, so this paper put forward a improved genetic algorithm - GM (1, 1) (IGA-GM (1, 1)), the proposed algorithm were used to solve the problem of short-term load forecasting...
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