Back-propagation neural networks for modeling complex systems

Abstract In complex engineering systems, empirical relationships are often employed to estimate design parameters and engineering properties. A complex domain is characterized by a number of interacting factors and their relationships are, in general, not precisely known. In addition, the data associated with these parameters are usually incomplete or erroneous (noisy). The development of these empirical relationships is a formidable task requiring sophisticated modeling techniques as well as human intuition and experience. This paper demonstrates the use of back-propagation neural networks to alleviate this problem. Backpropagation neural networks are a product of artificial intelligence research. First, an overview of the neural network methodology is presented. This is followed by some practical guidelines for implementing back-propagation neural networks. Two examples are then presented to demonstrate the potential of this approach for capturing nonlinear interactions between variables in complex engineering systems.

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