Multi-Valued Neurons: Hebbian and Error-Correction Learning

被引：0
|
作者：
Aizenberg, Igor [1 ]
机构：
[1] Texas A&M Univ Texarkana, Dept Comp Sci, Texarkana, TX 75505 USA
来源：
Advances in Computational Intelligence, IWANN 2011, Pt I | 2011年 / 6691卷
关键词：
complex-valued neural networks; derivative-free learning; multi-valued neuron; ASSOCIATIVE MEMORY; ALGORITHM;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we observe some important aspects of Hebbian and error-correction learning rules for the multi-valued neuron with complex-valued weights. It is shown that Hebbian weights are the best starting weights for the error-correction learning. Both learning rules are also generalized for a complex-valued neuron whose inputs and output are arbitrary complex numbers.
引用
收藏
页码：33 / 40
页数：8
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