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Radial Basis Function
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Matlab Functions for Radial Basis Function Networks
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Installation Instructions.
1. Unpack the distribution in some convenient folder.
2. Within this folder you will find a number of
subdirectories,
including
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径向基核函数测试函数，内含多个测试函数，可用于统计算法、支持向量机算法内核计算-Radial basis function kernel function test, includes a plurality of test functions
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rbf 径向基函数神经网格的代码-rbf radial basis function
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径向基（RBF）神经网络进行剩余寿命预测，训练输入为30*735的矩阵，输出为1*735-Radial Basis Function (RBF) neural network to predict the remaining life, the training input matrix of 30* 735, 735* 1 output
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用径向基网络预测地下水位源代码，给出了不同神经元个数下对应的均方差值以及训练、验证和测试曲线的图形-Radial basis function network forecasting of groundwater source, given the number of different neurons corresponding to the mean square difference and training, validation and test curve graphics
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利用BP神经网络与RBF（径向基函数）网络对行人、自行车、卡车三类目标进行分类，比较两种网络的分类性能，包含三类目标的数据信息，完整的代码-BP neural network and RBF (Radial Basis Function) network for pedestrians, bicycles, trucks three goals categorize performance comparison of the two networks, data information cont
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Radial basis function based err correction algorithm implementation for classification, system identification and function approxiamtion
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Radial Basis Function Classfier
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Radial Basis Function Neural Network for Tanker Ship Heading Regulation
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详细介绍了神经网络的原理及学习过程、径向基函数的选择及支持向量机的原理、前馈过程神经网络的时序处理及神经网络动态规划，有助于初学者深入了解神经网络的工作原理及运行机制。-Described in detail the neural network principle and learning process, radial basis function selection and support vector machine principle, feedforward process neur
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《MATLAB神经网络原理与实例精解》一书配套的径向基网络预测地下水位示例，含有完整注释。- MATLAB network principles and examples of fine nerve Solutions, a book supporting the radial basis function network forecasting of groundwater example, contains the complete comment.
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Good from matlab. radial basis function.
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matlab里面神经网络工具箱的例子锦集，主要包括了bp神经网络，随机神经网络、自组织竞争神经网络、径向基函数网络。-Inside matlab neural network toolbox Jinji examples, including the bp neural networks, Stochastic neural networks, since neural network, radial basis function network organization competition
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本案例是采用GRNN（径向基）神经网络预测水资源量，GRNN神经网络适合于小样本、高精度预测，本实例附有数据（不全），下载者可以直接将数据更改为自己的数据便可以使用。程序中使用了交叉验证方法，使预测精度大大提高-This case is the use of GRNN (Radial Basis Function) neural network to predict the amount of water, GRNN neural networks suitable for small samp
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有关神经网络的code，来源于论文“Efficient Object Tracking Using K means and Radial Basis Function”-About the neural network code, the paper " Efficient Object Tracking Using K means and Radial Basis Function"
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radial basis function
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a self-adjustment algorithm
based on GAP-RBF is proposed for solving how to
choose the overlap factorfor calculating neuron impact width.-In this paper, we propose a Constructive Enhancement
for OS-ELM (CEOS-ELM), which can add random
hidden
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rbf神经网络函数拟合。径向基函数，由样本选出固定中心值和delta-RBF neural network function fitting.Radial basis function (RBF), fixed center value chosen by the sample and the delta
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Compactly Supported Radial Basis Function Networks
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