Auto-Associative Neural Network Based on New Hybrid Model of SFNN and GRNN

Currently, associative neural networks are among the most extensively studied and understood neural paradigms. In this paper, we propose a hybrid model of neural network for associative recall of analog and digital patterns. This hybrid model consists of self-feedback neural network structures (SFNN) parallel with generalized regression neural network (GRNN). Firstly, patterns are stored as the asymptotically stable fixed points of the SFNN by using new learning algorithm developed by authors of this paper. In the retrieving process, each new pattern is firstly 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 demonstrate the effectiveness of the proposed hybrid model and simultaneously confirm the theoretical deductions.