Optimization of stochastic networks using simulated annealing for the storage and recalling of compressed images using SOM

• DWT and SOM together can be used to significantly enhance the recall efficiency of the HNN.
• Performance with DWT Filtered Patterns as compared to FFT Filtered patterns is better.
• SOM-HNN suffers from a high rate of spurious minima due to overlapping of the feature spaces.
• HNN can be optimized for reducing the effect of spurious minima, by employing Simulated Annealing in the recalling process.

In this paper we are studying the optimization of Stochastic Hopfield neural network and the hybrid SOM–Hopfield neural network for the storage and recalling of fingerprint images. The feature extraction of these images has been performed using FFT, DWT and SOM. The feature vectors are stored in the Hopfield network with Hebbian learning and modified Pseudoinverse learning rules. The study explores the tolerance of Hopfield neural networks for reducing the effect of spurious minima in the recalling process by employing the Simulated annealing process. It is observed from the simulations that the capabilities of the Hopfield network can be sufficiently enhanced by making modifications in the feature extraction of the input data. DWT and SOM together can be used to significantly enhance the recall efficiency. The probability of error in recall in the form of spurious minima is minimized by adopting simulated annealing process in the pattern recalling process.