Abstract
How to exploit current information techniques for rapidly and accurately building a fittest neural network becomes increasingly significant for flood peak forecasting. This paper firstly designs a distributed computing architecture and builds a computing environment based on Grid technologies. Then a distributed computing service for neural networks based on a genetic algorithm and a modified BP algorithm is designed and developed to rapidly and accurately building a fittest neural network for flood peak forecasting. Finally, a distributed computing prototype system is developed and implemented on a case study of the flood prevention in Shenzhen city, China. The experiment result shows that the scheme addressed in the paper is efficient and feasible.
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhu, J., Liu, C., Gong, J., Wang, D., Song, T. (2006). A Distributed Computing Service for Neural Networks and Its Application to Flood Peak Forecasting. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_98
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DOI: https://doi.org/10.1007/11893257_98
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
eBook Packages: Computer ScienceComputer Science (R0)