Multi-valued neurons: hebbian and error-correction learning

Authors:
Igor Aizenberg
Affiliations:
Department of Computer Science, Texas A&M University-Texarkana, Texarkana, TX
Venue:
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Year:
2011

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Abstract

In this paper, we observe some important aspects of Hebbian and errorcorrection learning rules for the multi-valued neuron with complex-valued weights. It is shown that Hebbian weights are the best starting weights for the errorcorrection learning. Both learning rules are also generalized for a complex-valued neuron whose inputs and output are arbitrary complex numbers.