Abstract
Symmetry detection task in the domain of 100-dimension binary vectors is considered. This task is characterized by practically infinite number of training samples. We train an artificial neural network with binary neurons to solve the symmetry detection task. Weight changing of hidden neurons is performed according to Pavlov Principle. In the presence of error, synaptic weights are adjusted considering a matrix of random weights. After training on a relatively small number of data samples our network obtained generalization ability and detects symmetry on data not present at the training set. The obtained averaged percentage of correct recognition of our network is better than those of classic perceptron with fixed weights of synapses of neurons of hidden layer. We also compare performance of different modifications of the architecture including different number of hidden layers, different number of neurons in hidden layer, different number of neurons’ synapses.
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Acknowledgements
The work is financially supported by State Program of SRISA RAS No. 0065-2019-0003 (AAA-A19-119011590090-2).
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Lebedev, A.E., Solovyeva, K.P., Dunin-Barkowski, W.L. (2020). The Large-Scale Symmetry Learning Applying Pavlov Principle. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research III. NEUROINFORMATICS 2019. Studies in Computational Intelligence, vol 856. Springer, Cham. https://doi.org/10.1007/978-3-030-30425-6_48
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DOI: https://doi.org/10.1007/978-3-030-30425-6_48
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