Throughout history, engineers have struggled to devise new and efficient techniques for analyzing structures. Today, the finite element analysis method is by far the most popular and reliable technique. However, this approach does have its limitations. Finite element analysis mandates the use of a computer. Consequently, engineers have encountered problems with lack of storage space and large solution times. One of the most successful solutions to this problem is the use of a renumbering algorithm.

Renumbering algorithms attempt to reduce the size of the matrices used in calculations by changing the ordering of the nodes of a structure. One algorithm, which concentrates on reducing both the profile and wavefront characteristics of a matrix, is profile front minimization-PFM. Since its introduction in 1983, PFM has undergone several changes. PFM uses a set of hueristic weights for determining the optimal nodal ordering. In 1993, the algorithm was modified and parametric studies of these weights were conducted on twenty-nine separate structure files to determine the overall improvement. These studies concluded that there is a significant improvement in the algorithm's performance when the "correct" weights are used.

The parametric studies provided a different heuristic weighting set for each of the given structures. These results had to be generalized to incorporate all twenty-nine structures and allow for the extrapolation to new structures. In order to do this, the results of the individual studies needed to be combined to produce a general prediction method for the heuristic weights based on a structures topology. A neural network with back propagation was used to incorporate the set of optimal weights into a general method for the prediction of weights for any structure. The results of the parametric study along with the use of the neural network is the focus of this paper.

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