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Classification and generation of images with a hierarchically connected neural network


Diane Sekou Abdel Kader – Post-graduate Student, Assistant, HVE Department of Control Engineering, Moscow State Institute of Radio Engineering, Electronics and Automation. E-mail:

The paper introduces a method for construction and learning of neural networks which is based on organization of hierarchical connectivity between neurons of feed-forward neural network and on the use of biologically inspired principles for neuronal selectivity formation. Proposed approach allows detection of meaningful features in input information image. Complexity of the features grows from lower layers of neural network to the upper ones. Detected features can be used to increase the quality of image classification. Moreover image generation is possible based on the set of high-level features via multi-stage decoding of their values. Results of modeling of neural network with hierarchical connectivity are presented for tasks of recognition and generation of handwritten symbol images. Finally application perspectives of the proposed approach are discussed in the some tasks of data mining and intellectual control.


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