提出了新颖的最优模糊聚类神经网络模型对机械手运动轨迹进行控制。
This paper presents a novel framework for trajectory tracking of robotic manipulators based on the optimal fuzzy clusting neural network system.
为了获得滤除噪声和细节保留两方面更好的平衡，提出了一种自适应模糊聚类神经网络。
AFCN (adapted fuzzy cluster neural network) is introduced to keep up trade-off between noise attenuation and detail preservation in image processing.
三是深入研究模糊聚类神经网络的结构优化方法，提出两种模糊聚类神经网络优化方案。
At last we deeply studies the methods of optimizing the structure of fuzzy clustering, and proposes two algorithms.
此聚类算法可以在线地划分输入数据，逐点地更新聚类，自己组织模糊神经网络的结构。
This clustering algorithm can on-line partition the input data, pointwise update the clusters, and self-organize the fuzzy neural structure.
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。
This paper proposed a new model of dynamic fuzzy Kohonen neural network (TGFCM), which was applied to the text 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。
Fuzzy cluster is one of the branches of knowledge discovery in database (KDD). And neural network is a good tool for clustering.
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。
By using the methods of subtraction clustering and adaptive fuzzy neural network, a sort of motion planner for the underwater vehicle was designed.
提出了一种基于模糊超球神经网络聚类与图像处理技术相结合的沉积微相识别方法。
In this paper we propose an automatic sedimentary facies identifying method based on combing fuzzy ellipsoidal neural networks and image process technology.
该模型利用模糊聚类技术确定系统的模糊空间和模糊规则数，利用BP算法调整模糊神经网络的权系数。
The fuzzy space and the number of fuzzy rules of this model are defined by the fuzzy clustering method and weight coefficients of the model are adjusted by the BP algorithm.
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。
A new fuzzy clustering method and a distributed neural network are introduced, and the theory and their applications are stressed.
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。
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.
分别使用模糊聚类方法、混合模糊神经网络、关联规则挖掘等知识发现方法对间歇过程中的配方、周期性污垢、操作策略规则等进行挖掘和处理。
Using the Fuzzy Cluster method, Hybrid Fuzzy Neural Network, Association rules mining methods, etc. find and excavate the recipes, periodic fouling, and operation strategy rule in the batch process.
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。
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.
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。
Then we introduce how to identify the TS model from sample data using fuzzy clustering algorithm and equivalent feedforward neural network.
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。
To improve the diversity of similar cases, similarity retrieval method based on partitioned clustering and General Fuzzy Min-Max neural network is developed.
有两个例子是我们的研究，如DNA微阵列数据使用DNA微阵列数据的模糊art模糊神经网络和基因聚类的癌症患者预后的预测。
Two examples are our research such as the prediction of prognosis for cancer patients from DNA microarray data using FNN and gene clustering for DNA microarray data using fuzzy ART.
针对模糊神经网络不能接受离散标称变量输入的缺陷，在CCT模糊神经网络和模糊聚类方法的基础上，提出了一种混合模糊神经网络建模方法。
A Hybrid fuzzy neural network modeling method was presented. On the basis of CCT fuzzy neural network and fuzzy cluster method, this method can deal with discrete variables input.
该方法借鉴了神经网络理论、模糊聚类算法和自适应模式识别法的优点，自动完成样本的分类与样件设计工作。
Based on the theory of neural networks, fuzzy clustering algorithm and adaptive pattern recognition, the method can be used to classify and design the sample workpiece automatically.
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。
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。
This case USES combined with fuzzy clustering and generalized regression neural network clustering algorithm for intrusion data classification.
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