Learning critical temperature for homomorphic ARG matching by self-organising Hopfield network

The authors previously (1995) presented a programming strategy to generate a homomorphic mapping between two attributed relational graphs (ARG) by the Hopfield network. Further, a self-organisation scheme was also introduced to learn the constraint parameter used in the energy function. In order to generate the desired mapping, the temperature parameter should be set to the critical value. Estimation of the critical temperature is an extremely difficult problem. In this paper, a heuristic learning algorithm is presented to estimate a suitable value for the temperature parameter for every model and scene pair to be matched. Experimental results showed that the learning algorithm is capable of compensating for the variations in the model and scene characteristics and the time step used to simulate the dynamic equations of the Hopfield network