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Modeling dynamic engineering processes when the governing equations are unknown
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DOI：10.1016/S0045-7949(97)00145-4.png)
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The paper describes a method of modeling the dynamic behaviour of continuous engineering processes, using artificial neural networks. The technique is applicable to situations where the differential equations governing the behaviour of a system are nonlinear and poorly understood, such as is the case for frost-heave and thaw-settlement processes in soils. A means of modeling the unknown component of governing differential equations is first described. A method of discretizing the neural network models of these equations is then illustrated, and the way in which these networks can be used to simulate the behaviour of a process is discussed. The proposed approach is proven to provide highly accurate results in a series of experiments simulating the nonlinear thermal behaviour of translucent solid materials. The paper concludes with an identification of several on-going areas of further development and application of the proposed tool. (C) 1998 Elsevier Science Ltd. All rights reserved.
Keyword:
NEURAL NETWORKS
TRUCK
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