A chaotic neural network for the attributed relational graph matching problem in pattern recognition

We propose a new algorithm based on a chaotic neural network to solve the attributed relational graph matching problem, which is an NP-hard problem of prominent importance in pattern recognition research. From some detailed analyses, we reach the conclusion that, unlike the conventional Hopfield neural networks for the attributed relational graph matching problem, the chaotic neural network can avoid getting stuck in local minima and thus yield excellent solutions. Experimental results also verify that this algorithm provides a more effective approach than many other heuristic algorithms for the attributed relational graph matching problem and thus has a profound application potential in pattern recognition.