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1·A semi-supervised learning system was proposed based on ART (adaptive resonance theory).

根据自适应谐振理论提出了半监督学习自适应谐振理论系统。

2·This paper presents a new adaptive phase selector with an adaptive resonance theory (ART) based neural network.

提出一种新的基于自谐振神经网络结构的自适应故障选相元件。

3·Second, ART(Adaptive Resonance Theory)is adopted to classify the residuals automatically, no apriori knowledge is required.

然后利用ART （自适应共振理论）网络对残差进行自动分类，不需要故障的先验知识。

4·Because that wavelet transform can effectively extract the characters, the Adaptive Resonance Theory (ART) Neural Networks has a good learning ability.

由于小波变换能有效地提取字符的结构特征，自适应共振(art)网络有很好的学习能力。

5·The new network model is formed by incorporating the two concepts of fuzzy set theory, closeness and closest principles, with adaptive resonance theory (ART).

利用人工神经网络法中的自适应共振理论优选钻头 ，将定性、定量优选因素作为输入层神经元 ，形成一种综合性选型方法 。

6·For learning document classification on line, the paper gives the semi-supervised learning fuzzy ART model (SLFART) based on adaptive resonance theory and the models algorithm.

为了对在线学习文档进行分类，本文根据自适应谐振理论给出了一个半监督学习模糊art模型(SLFART)及其算法。

7·An ART2 neural network based on adaptive resonance theory is put forward in this work to identify the damage of the structures and to realize the on-line self-study of the network.

目的基于自适应共振理论，提出一种基于ART2神经网络的结构损伤识别方法，以实现结构损伤识别的自主学习。

8·In order to improve the efficiency of information retrieval, this article introduces the Adaptive Resonance Theory (art) into the searching and classification of Chinese documents.

为了提高信息查询的效率，本文将自适应谐振神经网络引入中文文档搜索分类之中。

9·This paper combines the two aspects to recognize handwritten digits by using wavelet transform to extract feature and Adaptive Resonance Theory (ART) Neural Networks for Classification.

本文将二者结合起来，用小波变换抽取特征、用自适应共振art网络作模式分类器来识别手写数字。

10·Through the theory of adaptive resonance, we analyze ART 'operational principle and specific algorithm.

本文讨论了自适应谐振理论ART，分析了ART的工作原理，给出了ART的具体算法。