自组织特征映射是一种人工神经网络方法，可以同时实现模式识别和数据分类。
Self Organizing Map is a method of artificial neural network, which implements pattern recognition and data clustering simultaneously.
该系统采用基于自组织神经网络分类器，其在线分类结果与人工配皮的一致性在84%以上。
The system adopts the neuro-network sorter based on self-organization. The consistence of the on-line sorting result with artificial sorting is above 84%.
自组织特征映射人工神经网络对正常肝的识别正确率达84.8% ，对脂肪肝的识别正确率达90 .9%。
The neural network algorithm showed accuracy rate of 84.8% for normal liver and 90.9% for fatty liver.
人工神经网络由于其具有较好的自组织、自学习，多输入、多输出的能力，在预测方面已取得了广泛应用。
Artificial neural network was used widely because of its self organizing and self learning, much input and much output ability.
利用径向基人工神经网络（RBF）同时具有自组织神经网络和回归网络的优点，可以对缺失数据进行预测。
The RBF network possesses the advantages of Kohonen and regression networks. A test was performed to prove the effectiveness of RBF to complement the incomplete spatial information.
基于人工神经网络与模糊控制理论，对非线性系统提出了一种自组织模糊神经网络模型，并推导出一类新型学习算法。
Based on a neural network and the fuzzy control theory, this paper presents a self-organizing fuzzy-neural network for nonlinear systems, and develops a new learning algorithm.
基于人工神经网络与模糊控制理论，对非线性系统提出了一种自组织模糊神经网络模型，并推导出一类新型学习算法。
Based on a neural network and the fuzzy control theory, this paper presents a self-organizing fuzzy-neural network for nonlinear systems, and develops a new learning algorithm.
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