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C. Xu*, F. Bouchart# and I. Goulter*

*Department of Civil Engineering and Building, Central Queensland University, Rockhampton, Australia
#Department of Civil and Offshore Engineering, Heriot-Watt University, Edinburgh, United Kingdom

doi:10.4203/ccp.50.9.3

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C. Xu, F. Bouchart, I. Goulter, "Neural Networks for Hydraulic Analysis of Water Distributions Systems", in B.H.V. Topping, M.B. Leeming, (Editors), "Innovation in Computer Methods for Civil and Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 129-136, 1997. doi:10.4203/ccp.50.9.3

Abstract

A new approach to solving hydraulic networks using recurrent neural network concepts is proposed. The network equilibrium flow problem is first formulated in this new approach as an optimisation problem which minimises a modified content function subject to constraints on flow continuity at each node. This constrained optimisation is then transformed into an unconstrained optimisation using the Augmented Lagrange Multiplier method. The derivation of the cost function enables the minimisation problem to be transformed into a set of ordinary differential equations which can be implemented using artificial neural networks (ANNs), namely Gradient-type Hopfield neural networks. The performance of the neural network-based hydraulic solver is demonstrated by computer simulation of an example network.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 50 INNOVATION IN COMPUTER METHODS FOR CIVIL AND STRUCTURAL ENGINEERING Edited by: B.H.V. Topping and M.B. Leeming Paper IX.3 Neural Networks for Hydraulic Analysis of Water Distributions Systems C. Xu, F. Bouchart# and I. Goulter *Department of Civil Engineering and Building, Central Queensland University, Rockhampton, Australia #Department of Civil and Offshore Engineering, Heriot-Watt University, Edinburgh, United Kingdom doi:10.4203/ccp.50.9.3 purchase the full-text of this paper Full Bibliographic Reference for this paper C. Xu, F. Bouchart, I. Goulter, "Neural Networks for Hydraulic Analysis of Water Distributions Systems", in B.H.V. Topping, M.B. Leeming, (Editors), "Innovation in Computer Methods for Civil and Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 129-136, 1997. doi:10.4203/ccp.50.9.3 Abstract A new approach to solving hydraulic networks using recurrent neural network concepts is proposed. The network equilibrium flow problem is first formulated in this new approach as an optimisation problem which minimises a modified content function subject to constraints on flow continuity at each node. This constrained optimisation is then transformed into an unconstrained optimisation using the Augmented Lagrange Multiplier method. The derivation of the cost function enables the minimisation problem to be transformed into a set of ordinary differential equations which can be implemented using artificial neural networks (ANNs), namely Gradient-type Hopfield neural networks. The performance of the neural network-based hydraulic solver is demonstrated by computer simulation of an example network. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £52 +P&P)
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