Pattern recognition using a hierarchical neural network

A pattern recognition system based on hierarchical neural networks is proposed in this paper. The hierarchical system consists of two levels of networks: low-level for feature extraction and high-level for object recognition. The low-level is a competitive Hopfield neural network (CHNN) which detects the dominant points of a target shape to be the pattern features, based on the minimization of a cost function. The CHNN is implemented by incorporating a winner-take-all strategy in the network. By imposing the winner-take-all rule, one is relieved from deciding the suitable values of the weighting factors in the cost function. Furthermore, from the experimental results, the authors also find that the proposed CHNN performs very well in determining the dominant points of a target shape. After the features have been extracted, they are applied to the high-level multilayered network for object recognition. Because the multilayer network has high robustness to the pattern variations, the recognition system is found to possess high noise tolerance capability. Experimental results show that the system can recognize all the objects correctly when the percentage of noises is under 10%. Even when the percentage of noises reaches 40%, the recognition ratio is still over 90%