An assembly neural network for texture segmentation
Neural Networks
Neural Assemblies, an Alternative Approach to Artificial Intelligence
Neural Assemblies, an Alternative Approach to Artificial Intelligence
Handwritten Digit Recognition Using State-of-the-Art Techniques
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Flat image recognition in the process of microdevice assembly
Pattern Recognition Letters
The evolution of modular artificial neural networks for legged robot control
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Automatic design of modular neural networks using genetic programming
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Information Sciences: an International Journal
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The paper consists of two parts, each of them describing a learning neural network with the same modular architecture and with a similar set of functioning algorithms. Both networks are artificially partitioned into several equal modules according to the number of classes that the network has to recognize. Hebbian learning rule is used for network training. In the first network, learning process is concentrated inside the modules so that a system of intersecting neural assemblies is formed in each module. Unlike that, in the second network, learning connections link only neurons of different modules. Computer simulation of the networks is performed. Testing of the networks is executed on the MNIST database. Both networks directly use brightness values of image pixels as features. The second network has a better performance than the first one and demonstrates the recognition rate of 98.15%.