Digraph visualization using a neural algorithm with a heuristic activation scheme

This paper presents an activation scheme for use with Hopfield neural network algorithms that guarantees a valid solution for a particular category of problems. The technique monitors the appropriate neurons and heuristically controls their activation function. As a result it has been possible to eliminate several constraint terms from the energy function that normally would have been required to drive the network toward a valid solution. This saves time and eliminates the need for empirically determining a larger number of constants. This technique has been applied to the combinatorial optimization problem called hierarchical digraph visualization that arises in many application areas where it is necessary to visually realize the relationship between entities in complex systems. Results are presented that compare this new approach with a more traditional neural network approach as well as heuristic approaches, performance improvement in terms of the solution quality as well as execution time relative to both alternative techniques was achieved.