它支持有向或无向的模型，离散或连续的变量，各种推论及学习算法。
It supports directed and undirected models, discrete and continuous variables, various inference and learning algorithms.
实际问题中经常涉及连续的数值属性，然而许多归纳学习算法却是针对离散属性空间的。
The continuous attribute problems are often encountered in the real world, but many outstanding inductive learning algorithms are mainly based on a discrete feature space.
使用一个半监督学习算法，ANN可产生一个能够指示相对稳定度的连续分布的暂态稳定指标。
The ANN can derive a continuous-spread stability index to indicate the relative stability degree by means of a semi-supervised learning algorithm.
设计有效的学习算法快速准确地对脑电信号进行连续预测是脑机接口研究的关键之一。
To develop effective learning algorithms for fast and accurate continuous prediction using Electroencephalogram (EEG) signal is a key issue in BrainComputer Interface (BCI).
首先，提出一种模糊Q学习算法，通过模糊推理系统将连续的状态空间映射到连续的动作空间，然后通过学习得到一个完整的规则库。
A fuzzy Q learning algorithm is proposed in this dissertation, which map continuous state Spaces to continuous action Spaces by fuzzy inference system and then learn a rule base.
论文提出一种模糊强化学习算法，通过模糊推理系统将连续的状态空间映射到连续的动作空间，然后通过学习得到一个完整的规则库。
In this paper, we propose a fuzzy reinforcement algorithm, which map continuous state Spaces to continuous action Spaces by fuzzy inference system and then learn a rule base.
对一类连续系统的迭代学习控制问题进行了讨论，提出了一种新的迭代学习控制算法。
A discussion is made on the iterative learning control for a class of continuous systems.
基于误差反向传播的机制，针对连续制造过程的预测与控制，提出多层神经网络的逐个样本学习算法。
A one-by-one learning algorithm for multi-layer neural network modelling is presented based on the back-propagation mechanism of network error.
基于误差反向传播的机制，针对连续制造过程的预测与控制，提出多层神经网络的逐个样本学习算法。
A one-by-one learning algorithm for multi-layer neural network modelling is presented based on the back-propagation mechanism of network error.
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