By analyzing ART2 neural network clustering algorithm, an improved ART2 neural network clustering algorithm was proposed.
分析了现有ART2网络存在的问题，提出了一种改进的ART2算法。
This case USES combined with fuzzy clustering and generalized regression neural network clustering algorithm for intrusion data classification.
本案例采用结合模糊聚类和广义神经网络回归的聚类算法对入侵数据进行分类。
Whereas, an improved ART2 neural network clustering algorithm is proposed to realize the clustering of dynamic samples, and the simulation results are given out at the same time.
鉴于此，本文又提出了一种改进的ART2网络学习算法来实现动态样本的聚类，同时给出了该方法的实验仿真结果。
This neural network pattern recognition can be applied to feature extraction, clustering analysis, edge detection, signal enhancement and noise suppression, data compression, such as various links.
这样神经网络可应用于模式识别的特征提取、聚类分析、边缘检测、信号增强以及噪声抑制、数据压缩等各个环节。
Fuzzy cluster is one of the branches of knowledge discovery in database (KDD). And neural network is a good tool for clustering.
模糊聚类是目前知识发现(KDD)领域中的研究分支之一，而神经网络是用于聚类的良好工具。
This paper presents a new method of text clustering by using the latent semantic index (LSI) and self-organizing neural network (SNN).
根据隐含语义索引（LSI）理论和动态自组织映射神经网络理论，提出了一种文本聚类的新方法。
This paper proposed a new model of dynamic fuzzy Kohonen neural network (TGFCM), which was applied to the text clustering.
提出了一种新的动态模糊自组织神经网络模型(TGFCM)，并将其用于文本聚类中。
A new fuzzy clustering method and a distributed neural network are introduced, and the theory and their applications are stressed.
提出了一种新的模糊聚类方法，构造了分布式模糊神经网络，并重点附述了它们的原理与方法。
By using the methods of subtraction clustering and adaptive fuzzy neural network, a sort of motion planner for the underwater vehicle was designed.
使用减法聚类和自适应模糊神经网络方法设计了一种水下机器人运动规划器。
The paper presents an immune clustering RBF neural network (ICRBFNN) model for short-term load forecasting.
提出了一种免疫聚类径向基函数神经网络(ICRBFNN)模型来预测电力系统短期负荷。
In this paper, a method of information clustering and concept association is shown, it is based on neural network, and it aims at inkling information searching in knowledge discovery.
针对知识发现中的信息模糊查询问题，提出了一种基于神经网络的信息聚类及联想实现方法。
The whole network has not only the learning ability to neural network, but also the logic ability to fuzzy system based on rules, especially the automatic clustering ability to sub-class.
整个网络既有神经网络的学习能力，又有模糊系统的基于规则的推理能力，特别是对子类的自动聚类能力。
Then we introduce how to identify the TS model from sample data using fuzzy clustering algorithm and equivalent feedforward neural network.
然后介绍了如何使用模糊聚类算法和等价的前馈神经网络从样本数据中辨识离散的TS模型。
Self Organizing Map is a method of artificial neural network, which implements pattern recognition and data clustering simultaneously.
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。
Results show that Neural Network is an effective method for solving the weight evaluation, clustering analysis problem in mining and has widely spreading application prospect.
结果表明，神经元网络是用于解决采矿工程中权重评价、模式聚类问题的有效方法，对于解决采矿工程中相关问题有着广泛的应用前景。
Although ART2 neural network can well realize the clustering of dynamic samples, the problems arising from ART2 neural network itself restricted its application.
ART2神经网络虽能很好地实现这类动态数据的聚类，但ART2网络本身存在的问题又限制了它在这方面的应用。
The system identifier based on RBF neural network which applies nearest neighbor clustering algorithm realizes the identification of the inverse dynamic system model.
辨识器采用RBF神经网络结构和最近邻聚类算法，实现了对系统逆动力学模型的动态辨识。
To evaluate the sewing ability of the fabric objectively before being garment, a prediction system based on supervised fuzzy clustering neural network (SFCNN) for garment seams is proposed.
为在面料成衣之前客观评价其缝纫性能，提出了一种基于监督模糊聚类客观评价方法。
An ART2-based dynamic risk management model is proposed by using of clustering technique and neural network knowledge.
结合聚类技术和ART2神经网络技术提出一个基于ART2神经网络的动态风险管理模型。
A novel radial basis function neural network based on ant colony clustering (RBF-ACC) was proposed.
提出了一种基于蚁群聚类算法的径向基神经网络。
The model USES an improved nearest-neighbor clustering algorithm to select the RBF center, and a recursive least square algorithm to train weights of the RBF neural network.
该模型首先采用改进的最近邻聚类算法确定径向基函数中心，接着应用递推最小二乘法训练网络的权值。
The effect of samples training on BP neural network performance with the clustering characteristic of self-organizing competitive network is improved.
通过自组织竞争网络的聚类特征，改善样本训练对BP网络性能的影响。
To improve the diversity of similar cases, similarity retrieval method based on partitioned clustering and General Fuzzy Min-Max neural network is developed.
为解决相似案例差异性问题，提出了基于划分聚类和模糊神经网络的设计案例相似性检索方法。
RBF neural network has a strong clustering and fault-tolerant, and the training speed of RBF network is faster than the BP network, so RBF neural network is better on the whole.
RBF神经网络体现出较强的容错性和聚类性，且RBF网络的训练速度比BP网络快，整体而言效果更好。
Neural Network, as an important part of AI, is fit for the solution of complex clustering which with mass parameters.
神经网络是人工智能领域的重要组成部分，适合解决多参数的聚类分析等复杂问题。
Key attributes are used as inputs for learning by SOM neural network so as to obtain better clustering quality.
将关键属性作为SOM神经模型的输入，提高客户细分质量。
Key attributes are used as inputs for learning by SOM neural network so as to obtain better clustering quality.
将关键属性作为SOM神经模型的输入，提高客户细分质量。
应用推荐