Iterative autoassociative memory models for image recalls and pattern classifications

Autoassociative single-layer neural networks (SLNNs) and multilayer perceptron (MLP) models have been designed to achieve English-character image recall and classification. These two models are trained on the pseudoinverse algorithm and backpropagation learning algorithms, respectively. Improvements on the error-correcting effect of these two models can be achieved by introducing a feedback structure which returns autoassociative image outputs and classification tag fields into the network´s inputs. The two models are compared in terms of character image recall and classification capabilities. Experimental results indicative that the MLP network required longer learning time and a smaller number of weights, and showed more stable variations in noise-correcting capability and classification rate with respect to the change of the numbers of stored patterns than the SLNN