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.
首先研究了基于神经网络的故障诊断模型的分类和设计步骤，并将诊断模型大体分为6类和5个设计步骤；
First, it researches the category and design process of the fault diagnosis model based on neural network. The model includes 6 categories and 5 design processes.
研究了一类具有连续分布延时的反馈神经网络模型的周期解的存在性和全局稳定性。
Both the global exponential stability and the existence of periodic solutions for a class of recurrent neural networks with continuously distributed delays (RNNs) are studied.
对处于扰动状态下的预分馏塔的仿真结果表明，该算法可以有效地解决一类多神经网络模型的在线参数辨识问题。
Simulation results of a disturbed pre-fractionator show that this algorithm can be used to solve on-line parameters-recognized problem of a kind of multi- neural networks model effectively.
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.
椭球单元通过高斯分布逼近形成各模式类的决策区域，是一种非常适合于模式识别任务的前馈型人工神经网络模型。
Neural Networks with Ellipsoidal Activation Functions closes in upon a decision making region by Gauss distribution for various patterns and is adapted to fault diagnosis well.
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。
An ART2-based dynamic risk management model is proposed by using of clustering technique and neural network knowledge.
针对一类具有特殊模型的非线性系统本文提出了一种新型神经网络预测控制算法。
A novel neural network predictive control algorithm is proposed for a class of nonlinear system with special model.
摘要针对一类能够由中立型变延迟非线性微分方程描述的神经网络模型，给出了全局渐近稳定的不依赖于时间延迟的充分条件。
A sufficient condition guaranteeing the global asymptotical stability of the equilibrium point is derived for a class of neural network models with variable delay and neutral type delay.
本文研究了一类二元离散人工神经网络模型的解的收敛性及周期解的存在性等动力学特征。
This thesis has studied the dynamic features of a class of the discrete-time neural network model of two neurons, such as the convergence and periodicity and etc.
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。
The paper presents an immune clustering RBF neural network (ICRBFNN) model for short-term load forecasting.
讨论了一类二元时滞反馈人工神经网络模型。
This paper is concerned with a two-neuron artificial neural network model with delayed feedback.
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.
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.
针对一类不确定的离散时间非线性非最小相位动态系统，提出了一种基于神经网络和多模型的直接自适应控制方法。
A direct adaptive control approach is proposed for a class of uncertain discrete time nonlinear non-minimum phase dynamical systems.
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。
The system identifier based on RBF neural network which applies nearest neighbor clustering algorithm realizes the identification of the inverse dynamic system model.
一类多时滞不确定非线性系统，基于模型的模糊控制和神经网络控制相结合的混合控制方法。
A mixed control method combining fuzzy model-based control and neural network control is presented for a class of uncertain nonlinear system with multiple time delays.
提出了一种k-均值聚类算法和SOM自组织神经网络算法相结合的异常检测模型，使得系统可以更好的分类正常数据流和异常数据流，以此来防范未知的攻击。
Secondly, the anomaly detection model based on K-means algorithm and SOM network is constructed. It can classify the normal and abnormal network data stream so better to detect the unknown attack.
对时间序列的一类预测模型进行了研究，把灰色模型与BP神经网络模型组合建模，通过实例分析取得好的效果。
In this paper, by using the gray system theory and the dynamic BP neural network, the combination forecasting model are discussed.
在一类随机神经网络的研究中，一些研究人员提出马尔科夫神经网络模型。
In studying of a class of random neural network, some of relative researchers have proposed Markov model of neural network.
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.
讨论了一类具自反馈二元时滞神经网络模型的渐近行为 。
We consider a network of two neurons with feedback and delay.
第三章研究了一类具有变时滞的模糊bam神经网络模型的全局指数稳定性。
Chapter 3 introduces global exponential stability of a class of fuzzy BAM neural networks with variable delays.
对处于扰动状态下的预分馏塔的仿真结果表明，该算法可以有效地解决一类多神经网络模型的在线参数辨识问题。
Simulation results of a disturbed pre-fractionator show that this algorithm can be used to solve on-line parameters-recognized problem of a kind of multi- neural …
对处于扰动状态下的预分馏塔的仿真结果表明，该算法可以有效地解决一类多神经网络模型的在线参数辨识问题。
Simulation results of a disturbed pre-fractionator show that this algorithm can be used to solve on-line parameters-recognized problem of a kind of multi- neural …
应用推荐