These two ARTs have obtained broad applications due to both their simple learning rules and dynamical online learning behaviors.
由于两类模型的简单学习规则和动态在线学习特性使其获得了广泛的应用。
This paper provides a new architecture of neural network, called Loop architecture neural network (LANN), and its learning rules.
本文提出了一种称为环结构神经网络(LANN)模型及其学习算法。
Neuron is the basic unit of Neural Network. In the Neural Controller, the learning process of synaptic weights has a close relation with the learning rules.
神经元是构成神经网络的基本单元，在神经元控制器中，突触权因子（加权因子）的学习过程与神经元学习规律有着密切的关系。
An adaptive neuron control method is presented. By use of this kind of controller, an intelligent control system is designed, and its learning rules are also presented.
该文给出了一种自适应神经元的控制方法，设计了神经元网络作为控制器的智能控制系统，给出了学习规则。
As the basic unit of Neural Network, Neural Controllers with different learning rules will result in different control effects for the learning process of synaptic weights.
作为神经网络控制的基本单元，采用不同学习规则的神经元控制器，对神经元的学习过程将产生不同的影响。
The problem of blind separation of signals in post-nonlinear mixture is addressed. The learning rules for the general post-nonlinear separation structure are derived by a maximum likelihood approach.
研究后非线性混合信号的盲分离，从最大似然角度推导了一般后非线性分离结构的学习公式；
Learning rules without examples is useless.
学习规则而不记例子是无用的。
Learning rules are constructed according to deterministic annealing to optimize classifier parameters, on purpose to reduce classification error and system entropy of the space to be identified.
由确定性退火技术构造学习规则用于优化分类器参数，目的是减少分类误差以及待识别空间的系统熵。
Learning rules are constructed according to deterministic annealing to optimize classifier parameters, on purpose to reduce classification error and system entropy of the space to be identified.
由确定性退火技术构造学习规则用于优化分类器参数，目的是减少分类误差以及待识别空间的系统熵。
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