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rbf networks     
相关语句
  rbf网络
     Application of Data Fusion Based on RBF Networks for Fault Diagnosis of SAMS
     基于RBF网络的信息融合在SAMS故障诊断中的应用
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     Fault diagnosis method of rotary machinery based on RBF networks
     基于RBF网络的旋转机械故障诊断方法
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     Incremental learning using RBF networks
     基于RBF网络的增量学习
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     Studies on RBF networks compensative control of chaotic system
     混沌系统的RBF网络补偿控制方法研究
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     STRUCTURE OPTIMIZATION STRATEGY OF NORMALIZED RBF NETWORKS
     归一化RBF网络的结构优化策略(英文)
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  rbf神经网络
     Nonlinear PCA fault detection method based on RBF networks
     一种基于RBF神经网络的非线性PCA故障检测方法
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     Generalized Fuzzy Inference and Generalized Fuzzy RBF Networks
     广义模糊推理与广义模糊RBF神经网络
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     Nonlinear model predictive control based on RBF networks
     基于RBF神经网络的非线性模型预测控制
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     Adaptive Fuzzy RBF Networks Learning for Autonomous Multi-robots
     自适应模糊RBF神经网络的多智能体机器人强化学习
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     In order to improve rolling force prediction accuracy,a model was proposed with a geometrical relationship to determine roll contact area and RBF networks to predict unit rolling force which is affected by many factors.
     为提高轧制过程轧制力预报精度,建立了将轧制接触面积由几何关系确定,将影响因素复杂的轧制单位压力通过RBF神经网络预测模型。
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  rbf网
     This paper proposes a novel RBF networks,PRBF networks,which is based on the potential function algorithm,while introducing the mechanism and the algorithm of general RBF networks and pointing out its some drawbacks. The experiments show that PRBF spends very short time in training,and can overcome the defects of the general RBF,and has good classification ability.
     阐述了标准RBF网的机理和算法,指出其算法上存在的不足,进而引入基于势函数算法的RBF网(简称势RBF网).通过实验论证了势RBF网不仅训练速度快,而且能克服标准RBF网的不足,具有良好的分类效果
短句来源
     The key point in design of RBF networks is to specify the number and locations of the centers. The adaptive method combines the advantages of IOC and ROLS algorithms. Not only the number but also the positions of data centers are adapted in learning progress, which optimizes the structure of nets very well.
     RBF 网设计的核心在于确定网络中心的数目及位置,该自适应算法有效地融合了 IOC 与 ROLS 算法的优点,不仅能动态调节RBF 网的隐节点数,还能使网络的数据中心自适应变化,很好地优化了网络的结构。
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     Moreover,the experiments on a closed set, text-independent speaker recognition system show that, better robustness and simpler networks can be achieved through improved adaptive RBF networks in comparison with classical RBFN.
     用与文本无关的闭集说话人识别系统对该算法进行了验证,实验结果表明,该方法与传统的 RBF 算法相比,自适应 RBF 网具有较好的鲁棒性以及精简的网络结构等优点。
短句来源
  “rbf networks”译为未确定词的双语例句
     Application of RBF Networks to Cloud Detection
     径向基函数网络在云检测中的应用
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     ON-LINE Training RBF Networks Based On APC-ⅢCluster Algorithm
     基于APC-Ⅲ算法在线训练径向基神经网络
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     The Study of RBF Networks and Its Application to Complex Chemical Information Processing
     RBF神经元网络的研究及其在复杂化学信息处理中的应用
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     Internal Model Control of CSTR Based on RBF Networks
     基于RBF网的CSTR内模控制
     The Study of RBF Networks and Genetic Algorithms and Their Application to Chemical Engineering
     RBF神经元网络和遗传算法的研究及其在化工中的应用
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  rbf networks
An approach for structural parametric synthesis of the RBF networks constituting the classifier basis is developed.
      
Therefore, we have developed several computational-geometry-based algorithms that regularize the data before computing a surface estimation using RBF networks.
      
We report a method using radial basis function (RBF) networks to estimate the time evolution of population activity in topologically organized neural structures from single-neuron recordings.
      
Adaptive Multiuser Detection Based on RBF Networks in Impulsive Noise CDMA Channels
      
For such, several methods proposed in the literature for optimizing RBF networks using Genetic Algorithms are discussed.
      
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The application of radial basis Function (RBF) ANN model to the daily peak load and daily 24-hour load in power system is proposed. The structure of the RBF network is presented first, then, the clustcr technique and orthogonal learning algorithm are used for determining the RBF network's centers and for network training. The effectiveness of the presental foreasting strategy is demonstrated by baning and triting using the data collected from Jing -Jin-Tang power network.

将RBF(RadiaBasisFunctio.辐基函数)人工神经网络模型用于电力系统日峰值负荷与日小时负荷的预测。文中首先给出了RBF网络的结构,然后讨论确定RBF网络中心及网络训练的聚类法和正文化法。利用从京津唐系统中收集到的负荷数据进行网络模型的训练和回响检测,所得结果证实了ABF网络用于负荷预测的有效性。

RBF neural networks provide a powerful technique to model nonlinearmapping.The learning algorithm for RBF networks corresponds to the solution of alinear problem, therefore fast. However, if the data are contaminated by additivenoise,the approximation function will oscillate rapidly. As a result,the generalizationproperties are restricted. Some smoothing method had been advanced in earner works.A new smoothing method is presental in this paper. Experimental results show thatthe generalization...

RBF neural networks provide a powerful technique to model nonlinearmapping.The learning algorithm for RBF networks corresponds to the solution of alinear problem, therefore fast. However, if the data are contaminated by additivenoise,the approximation function will oscillate rapidly. As a result,the generalizationproperties are restricted. Some smoothing method had been advanced in earner works.A new smoothing method is presental in this paper. Experimental results show thatthe generalization properties are improved,

用RBF网络为非线性映射建模,其学习算法对应于求解线性问题,因而学习速度快。然而在样本数据含有加性噪声的情况下,拟合函数会出现迅速振荡,使推广能力受到限制。过去曾提出过一些平滑算法。本文提出了一个新的平滑算法。实验结果表明,网络的推广能力有进一步的提高。

Automatic classification of communication signals can be considered as a pattern recognitionproblem. It can be solved by davital signal processing and pattern recognition. This Paper presents a method to solve this problem by using RBF networks. A structure of neural networks isdesigned by means of the RBF network for recognizing communication signals. Simulation experboantat results show that, owing to using feature Valors to lower dimensions, the new networkscan not only accomplish the signal...

Automatic classification of communication signals can be considered as a pattern recognitionproblem. It can be solved by davital signal processing and pattern recognition. This Paper presents a method to solve this problem by using RBF networks. A structure of neural networks isdesigned by means of the RBF network for recognizing communication signals. Simulation experboantat results show that, owing to using feature Valors to lower dimensions, the new networkscan not only accomplish the signal recognition task, but also give a better result than the existingmethods in terms of training speed, memory robustness and realization by using hardware.

通信信号自动分类是一模式识别问题,通常用数字信号处理和模式分类的方法来求解.文中提出了将RBF(RadialBasisFunction)网络方法应用于通信信号自动识别的具体方法.构造了运用RBF网络的信号分类的神经网络结构.通过模拟实验表明,由于采用了将信号特征矢量降维的方法,该网络不仅能够很好地完成信号分类,而且具有比传统方法训练速度快、占用存贮空间少、容错性强和易子硬件实现等特点.

 
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