NDLI: A novel water quantities allocation arithmetic in water management

Content Provider	IEEE Xplore Digital Library
Author	Chen Xu-sheng Fan De-cheng
Copyright Year	2010
Description	Author affiliation: School of Economics and Management, Harbin Engineering University, China (Fan De-cheng) \|\| School of Management, Management Science and Engineering Postdoctoral Researcher Flow Station, Harbin University of Science and Technology, China (Chen Xu-sheng)
Abstract	A water quantities allocation arithmetic was proposed, Radial basis function neural network (RBFNN) was designed, and simulated annealing arithmetic was adopted to adjust the network weights. MATLAB program was compiled; experiments on related data have been done employing the program. All experiments have shown that the arithmetic can efficiently approach the surface with $10^{−4}$ mm error precision, also the learning speed is quick and predictions is ideal. Trainings have been done with other networks in comparison. Back-propagation learning algorithm network does not converge until 2000 iterative procedure, and exactness design RBFNN is time-consuming and has big error. The arithmetic can approach nonlinear function by arbitrary precision, and also keep the network from getting into partial minimum.
Starting Page	309
Ending Page	313
File Size	129054
Page Count	5
File Format	PDF
ISBN	9781424458455
e-ISBN	9781424458486
DOI	10.1109/ICACC.2010.5487070
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time series analysis water management Analytical models Technology management Engineering management RBFNN Radial basis function networks Simulated annealing water quantities allocation arithmetic Resource management Mathematical model simulated annealing arithmetic Water resources Arithmetic
Content Type	Text
Resource Type	Article

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