Two-value image data compressing and recovering using improved neural network

Data compression and generalization capability are important characteristics of a neural network model. From this point of view, the two-value image data compression and recovering of a hybrid neural network are examined experimentally. The applied neural network models are the improved ART1 and the feedforward types. The hybrid network architecture, its learning process and the improved learning algorithm are presented in this paper. The whole work has been finished using a large scale general-purpose neural network simulating system, the GKD-N/sup 2/S/sup 2/ on the SUN3 workstation. Some experimental results also have been given and are discussed.<
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