Implementing RNN with Non-Randomized GA for the Storage of Static Image Patterns

Abstract

The hybridization of evolutionary technology has been extensively used to enhance the performance of recurrent type neural networks (RTNN) for storing patterns and their recalling. Several experiments have been done to link evolutionary processes such as genetic algorithm (GA) with RTNN regarding the connection weight among the processing elements. This integration strengthens the efficiency of the Recurrent neural network (RNN) to effectively recall the increased capacity and patterns of sample storage to reduce the flaw of local minima. Bipolar product rule (BPR) has been applied predominantly for pattern storage, and GA are further used to develop the weight matrix to explore the global optimal solution reflecting the correct invocation of the storage pattern. Here, Edge Detection (ED) and self-organizing map (SOM) methods are applied for the purpose of feature extraction. The modified BPR and GA have been employed to store patterns, and recalling respectively. The proposed hybrid RTNN performance is examined for the handwritten Greek symbols.