SELFLEARNING FUZZY SPIKING NEURAL NETWORK BASED ON DISCRETE SECOND-ORDER CRITICALLY DUMPED RESPONSE UNITS FOR FUZZY CLUSTERING TASKS

Y.V. Bodyanskiy, A.I. Dolotov, D.M. Malysheva

Abstract


Hybrid neural networks based on the idea of combining spiking neural networks and the principles of fuzzy logic are considered. The architecture of self-learning fuzzy spiking neural network based on discrete second-order critically damped response units is proposed. It is proposed to define a spiking neural network in terms of apparatus of classical automatic control theory based on the Laplace transform and z-transform. It is shown that a spiking neural network is a pulse-position threshold detection system based on second-order damped response units. Such kind of description allows using it as an analog-digital system in technical problems solving. The output layer takes firing times of spikes arriving from the second layer, and either performs fuzzy partitioning of the input patterns using probabilistic approach.

Keywords


fuzzy clustering, spike, fuzzy spiking neural network, classical automatic control theory, second-order damped response units, pulse-position threshold detection system.

GOST Style Citations






DOI: https://doi.org/10.15588/1607-3274-2012-2-23



Copyright (c) 2014 Y.V. Bodyanskiy, A.I. Dolotov, D.M. Malysheva

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