Search - Training RBF Networks

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[StatusBar] r43 DL : 0: 鲁棒控制器设计，由于RBF网络可以实现任意逼近的非线性关系，它的目标是要做到误差平方和最小，与非线性PCA的目标一致，所以上述非线性PCA的模型可以通过采用两个RBF网络来实现非线性正变换和反变换。RBF网络是一个三层前馈网络，隐层采用径向基函数作为激励函数。第一个RBF网络把高维空间的数据映射到低维空间（如图4），第二个RBF网络将前面网络输出的低维空间数据再映射到高维空间，实现数据恢复(如图5)。这两个网络分别进行训练。-robust controller design, as RBF networks can achieve arbitrary nonlinear approximation, Its goal is to achieve the minimum squared error, and nonlinear PCA have the same goal So these nonlinear PCA model may be adopted by two RBF networks to achieve nonlinear transformation and inverse transform. RBF network is a feed-forward network, hidden layer RBF function as an incentive. RBF a network of high-dimensional data mapping space to the low-dimensional space (figure 4), second RBF network will be in front of the output of low-dimensional space mapping data again to a high-dimensional space. data Recovery (figure 5). The two networks separately for training.
Date : 2008-10-13 Size : 1.51kb User : 浇洒距离
[StatusBar] r43 DL : 0: 鲁棒控制器设计，由于RBF网络可以实现任意逼近的非线性关系，它的目标是要做到误差平方和最小，与非线性PCA的目标一致，所以上述非线性PCA的模型可以通过采用两个RBF网络来实现非线性正变换和反变换。RBF网络是一个三层前馈网络，隐层采用径向基函数作为激励函数。第一个RBF网络把高维空间的数据映射到低维空间（如图4），第二个RBF网络将前面网络输出的低维空间数据再映射到高维空间，实现数据恢复(如图5)。这两个网络分别进行训练。-robust controller design, as RBF networks can achieve arbitrary nonlinear approximation, Its goal is to achieve the minimum squared error, and nonlinear PCA have the same goal So these nonlinear PCA model may be adopted by two RBF networks to achieve nonlinear transformation and inverse transform. RBF network is a feed-forward network, hidden layer RBF function as an incentive. RBF a network of high-dimensional data mapping space to the low-dimensional space (figure 4), second RBF network will be in front of the output of low-dimensional space mapping data again to a high-dimensional space. data Recovery (figure 5). The two networks separately for training.
Date : 2025-07-04 Size : 1kb User : 浇洒距离
[AI-NN-PR] rbf1 DL : 0: 此源代码是用MATLAB训练RBF网络，用的是数据中心聚类法，算法中没有用MATLAB中的训练函数-This source code is used MATLAB training RBF networks, data centers are using a clustering method, the algorithm does not use the training function in MATLAB
Date : 2025-07-04 Size : 7kb User : 张媛
[AI-NN-PR] RBFMIP DL : 0: Date : 2025-07-04 Size : 4kb User : wsy
[matlab] RBF_Training_radial_basis_neural_networks_with_th DL : 0: RBF. Training radial basis neural networks with the extend Kalman filter
Date : 2025-07-04 Size : 195kb User : Pyhesty
[Other] Haykin DL : 0: Dan Simon, Training radial basis neural networks with the extended Kalman Filter, 2001. Article on RBF neural networks, with examples, source programs on Matlab
Date : 2025-07-04 Size : 1.63mb User : strannik
[Special Effects] MATLAB-code DL : 0: 包含各种常见的MATLAB程序，如图像去噪、图像识别、RBF神经网络的训练，三次样条插值、求解线性方程组等几十个程序。-MATLAB contains a variety of common procedures, such as image denoising, image recognition, training RBF neural networks, cubic spline interpolation, linear equations, such as dozens program
Date : 2025-07-04 Size : 5.58mb User : 周
[AI-NN-PR] psoRBFweidai DL : 0: 先用PSO对RBF的权值进行训练，将训练好的网络用于微带线的建模-training the RBF networks with PSO, establish the microstip modeling with the trained networks
Date : 2025-07-04 Size : 1kb User : 邢飞
[AI-NN-PR] PNN网络代码 DL : 0: 概率神经网络(Probabilistic Neural Network)是由D.F.Speeht博士在1989年首先提出，是径向基网络的一个分支，属于前馈网络的一种。它具有如下优点：学习过程简单、训练速度快；分类更准确，容错性好等。从本质上说，它属于一种有监督的网络分类器，基于贝叶斯最小风险准则。(Probabilistic neural network was first proposed by Dr. D.F.Speeht in 1989. It is a branch of radial basis networks and belongs to a feedforward network. It has the following advantages: the learning process is simple, the training speed is fast, the classification is more accurate, and the fault tolerance is good. Essentially, it belongs to a supervised network classifier based on the Bayes minimum risk criterion.)
Date : 2025-07-04 Size : 5kb User : gahuan
[matlab] PNN DL : 0: 概率神经网络(Probabilistic Neural Network)是由D.F.Speeht博士在1989年首先提出，是径向基网络的一个分支，属于前馈网络的一种。它具有如下优点：学习过程简单、训练速度快；分类更准确，容错性好等。从本质上说，它属于一种有监督的网络分类器，基于贝叶斯最小风险准则。(The rate neural network, first proposed in 1989, is a branch of the RBF network and is one of the feedforward networks. It has the following advantages: the learning process is simple, the training speed is fast, the classification is more accurate, the fault tolerance is good, and so on. In essence, it belongs to a supervised network classifier based on Bayesian minimum risk criteria.)
Date : 2025-07-04 Size : 45kb User : 哼哼1214