Programming Hopfield network for object recognition

This paper investigates the performance of the Hopfield neural network as a constraint satisfaction network for invariant pattern recognition. Although the Hopfield network is known to provide instantaneous solution to optimization problems with combinatorial complexity, in some instances the solution is invalid. In this paper, we study a number of energy function formulations and experimentally explore their merits. We also present an industrial application of Hopfield network in recognizing transparent flexible membrane printed circuits and a subgraph isomorphism of synthetic line patterns invariant of position, scale and orientation. The proposed network can correctly recognize overlapped partial line patterns and offers highly parallel implementation