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In this paper, two modeling methods are studied for billet temperature prediction model based on the actual production data of the reheating furnace in Tangsteel 1700 line. The two methods are the neural network and the support vector machine. Two prediction models are built by the two methods respectively. And the comparative research is done via MATLAB simulation aiming at the two modeling methods...
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...
For supplying optimal scheduling of Zhengzhou city with short-term water consumption data, this paper builds three types of forecasting model according to moving arithmetic mean method, regression analysis method and BP neural network. As a result, forecasting result is obtained by water supply data and meteorological data. The study shows that three different methods all can meet the need of urban...
In order to avoid the economic loss due to too much or too little of electricity consumption, electricity consumption needs to be predicted. In order to solve the drawbacks of BP neural network, genetic algorithm and RBF neural network (GA-RBFNN) is presented to forecast electricity consumption in the study, and genetic algorithm is introduced and tried in optimizing the parameters of RBF neural network...
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