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- W2363503295 abstract "The state estimation model of water distribution network, which establishes the relation between unknown junction pressure of pipes and known monitor information, can been seen as a nonlinear function Estimator implementing the nonlinear mapping of input and output data. Moreover, support vector machine (SVM) is a good nonlinear function estimator. SVM is used to establish a state estimation model and a macroscopic model of monitor pressures. According to simulating analysis, the absolute error of 90% forecasting data is smaller than 0. 01 MPa. Compared with BP neural network, under the same mean square error of samples, the SVM approach has higher precision of the mean square error of testing sets. The application examples in Hangzhou city show that the relative error of 85% forecasting junction pressure is smaller than 5%, which is considerably perfect." @default.
- W2363503295 created "2016-06-24" @default.
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- W2363503295 date "2005-01-01" @default.
- W2363503295 modified "2023-09-25" @default.
- W2363503295 title "State estimation model of water distribution network based on SVM" @default.
- W2363503295 hasPublicationYear "2005" @default.
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