类神经网络的特色是由一些互相连接的处理器或节点组成。
A neural network typically consists of a number of interconnected processors, or nodes.
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.
文章阐述了类神经网络的由来、基本原理，并介绍了类神经网络在各领域的应用。
This article has introduced the origin and basic principles of neural network, and its applications in various fields.
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.
本文研究了一类神经网络优化特性的一些问题。对其解的特性、可行解空间的划分及吸引区特性进行了研究。
Some properties of neural optimization are studied in this paper. The properties of the solutions and the region of attraction, the division of the feasible solution space are investigated.
透过本研究创建的两组类神经网络为主架构，可经由灰关联分析所选定之特定电测资科做为类神经网络的输入值，预测地层特性参数。
The neural network we have built can be used to integrate and analyze the different kinds of well logging data to obtain the formation parameters in particular depth.
神经网络的出现为一类输入输出关系呈高度非线性的系统提供了建模的有效工具。
The emergence of neural network provide effective tools for a system with highly nonlinear input-output relations.
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.
模式识别成为人工神经网络特别适宜求解的一类问题。
Artificial neural network pattern recognition has become especially suitable for solving a class of problem.
提出利用直接自适应模糊神经网络控制一类不确定非线性混沌系统新方法。
A novel direct adaptive fuzzy neural networks (FNNs) controller for a class of uncertain nonlinear chaotic system is presented.
基于模糊神经网络算法研究了非线性系统的噪声消除问题，设计了一类非线性自适应逆噪声消除控制器。
Based on Fuzzy Neural Network, the noise canceling problem of the nonlinear system was studied. A type of nonlinear adaptive noise controller was proposed.
该文首先引进了一类粗糙神经网络算法，研究了其性质、特点。
The paper firstly introduces a kind of algorithm based on rough neural networks and studies its properties, features.
提出了一种减聚类径向基函数神经网络的纺织空调送风风机故障诊断方法。
A new method of subtractive clustering RBF network for air condition breeze fan fault diagnosis is presented.
第三类观点以联结和人工神经网络概念为要素，分别探讨了心理场距离和规避损失偏向对选择偏好的影响。
The third one, based on concepts of connectionist and artificial neural network, explored the effects of field distance and loss aversion bias respectively on choice preference.
在对径向基概率神经网络进行理论分析基础上，采用减法聚类方法确定它的隐中心矢量。
On the basis of analyzing RBPNN in theory, subtractive clustering is used to determine its hidden centric vector.
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.
本文提出了一种源于汉明类多层前向神经网络分组码译码器。
This paper presents a neural network decoder of linear block codes which originates from Hamming network.
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.
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.
目前光学图像识别主要有光学相关和光学神经网络两类实现方法。
At present there are two realization ways of optical image recognition: optical correlation and optical nerve network.
本文讨论了一类脉冲神经网络的周期解的指数稳定性。
This paper discusses the exponential stability of periodic solution for impulsive neural networks.
对于一类复合型模糊神经网络，论文首先进行了多方面的改进研究尝试。
This paper researched on a class of compound fuzzy neural network and made a variety of improvements.
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.
本文研究一类非线性神经网络自适应控制系统，提出一种基于双误差——辨识误差和跟踪误差的新控制方案。
A class of nonlinear neural network adaptive control systems is studied and a new design concept based on double errors was proposed in this paper.
提出了一种基于蚁群聚类算法和裁剪方法的RBF神经网络优化算法。
An optimization algorithm of RBF Neural Networks based on ant colony clustering and a pruning method is proposed.
研究了一类随机变时滞递归神经网络的几乎指数稳定性问题。
The almost surely exponential stability of stochastic recurrent neural networks with time-varying delays is investigated.
本文讨论了一类联想神经网络在学习过程中结构变化引起网络平衡点状态变化的动态特性；研究了网络的指数稳定性质；
The exponential stability and trajectory bounds of the motions of equilibria of an associative neural network under structural variations while learning a new pattern are investigated.
本文讨论了一类联想神经网络在学习过程中结构变化引起网络平衡点状态变化的动态特性；研究了网络的指数稳定性质；
The exponential stability and trajectory bounds of the motions of equilibria of an associative neural network under structural variations while learning a new pattern are investigated.
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