A Noisy Chaotic Neural Network for Solving Attributed Relational Graph Matching Problem

In this paper we propose a new gradual noisy chaotic neural network (MP-NCNN) to solve the NP-complete attributed relational graph matching problem. These graphs are very important in pattern matching applications and the noisy chaotic behavior of the proposed method which avoids getting trapped in local minima, yields in better results and hence it is more effective approach in comparison with previous methods. The performance of the proposed method has been evaluated through several attributed relational graphs with different permuted vertices and also with different vertex numbers. The obtained results show that the proposed method outperforms previous approaches including HNN, CNN and TCNN methods.

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