Auto-associative memory based on a new hybrid model of SFNN and GRNN: Performance comparison with NDRAM, ART2 and MLP
Hamed Davande, Mahmood Amiri, Alireza Sadeghian, Sylvain Chartier · 2008
Currently, associative neural networks (AsNNs) are among the most extensively studied and understood neural paradigms. In this paper, we use a hybrid model of neural network for associative recall of analog and digital patterns. This hybrid model which consists of self-feedback neural network structures (SFNN) parallel with generalized regression neural network (GRNN) were first proposed by authors of this paper. Firstly, patterns are stored as the asymptotically stable fixed points of the SFNN. In the retrieving process, each new pattern is applied to the GRNN to make the corresponding initial conditions of that pattern which initiate the dynamical equations of the SFNN. In this way, the corresponding stored patterns and noisy version of them are retrieved. Several simulations are provided to show that the performance of the hybrid model is better than those of recurrent associative memory, feed-forward multilayer perceptron and is equally comparable with the performance of hard-competitive models.