Description: 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. Platform: |
Size: 1024 |
Author:浇洒距离 |
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Description: This program demonstrates some function approximation capabilities of a Radial Basis Function Network.
The user supplies a set of training points which represent some "sample" points for some arbitrary curve. Next, the user specifies the number of equally spaced gaussian centers and the variance for the network. Using the training samples, the weights multiplying each of the gaussian basis functions arecalculated using the pseudo-inverse (yielding the minimum least-squares solution). The resulting network is then used to approximate the function between the given "sample" points. -This program demonstrates some function a pproximation capabilities of a Radial Basis Fu nction Network. The user supplies a set of train ing points which represent some "sample" point s for some arbitrary curve. Next, the user specifies the number of equally spaced Response centers and the variance for the netwo rk. Using the training samples, the weights multiplying each of the Gaussian ba sis functions arecalculated using the pseudo- inverse (yielding the minimum least-squares s middleware). The resulting network is then used to approximate the function between the given "sa mple "points. Platform: |
Size: 18432 |
Author:陈伟 |
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Description: RBF网络用于函数逼近的一个程序！内部包含几个关于RBF网络的几个算法。-RBF network for function approximation of a process! Internal contains several of the RBF network of several algorithms. Platform: |
Size: 1024 |
Author:武建 |
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Description: 基于RBF神经网络整定的PID控制
径向基函数具有单隐居的三层前馈网络。是—种局部逼近网络，己证明它能以任意精度逼近杠意连续函数。
-RBF neural network based tuning of PID control of radial basis function with the seclusion of the three single-feed-forward network. Yes- different local approximation network, it has been proved to arbitrary precision bars intended continuous function approximation. Platform: |
Size: 1024 |
Author:feiyang |
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Description: 主要完成对RBF网络用于函数逼近的功能,是一种在逼近能力、分类能力和学习速度等方面均优于BP网络的网络。
-Completion of the RBF network primarily used for function approximation function, is an approximation ability and classification ability and learning speed, etc. are better than BP network of networks. Platform: |
Size: 1024 |
Author:kk |
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Description: 由于本人近阶段在研究神经网络方面的，所以把有关方面的共享给大家。
这段是用rbf函数逼近的源码。可直接编译运行-Due to recent phase I study of neural networks, so the parties to share to everyone. This is the source function approximation rbf. Direct the compiler to run Platform: |
Size: 1024 |
Author:张芳 |
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Description: 模糊神经网络实现函数逼近与分类，实现模糊规则的提取。-Fuzzy neural network function approximation and classification, to achieve the extraction of fuzzy rules. Platform: |
Size: 463872 |
Author:王宁 |
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Description: Radial basis functions are used for function approximation and interpolation. This package supports two popular classes of rbf: Gaussian and Polyharmonic Splines (of which the Thin Plate Spline is a subclass).
The package also calculates line integrals between two points.
For more information, see blog.nutaksas.com for academic papers.
Platform: |
Size: 10240 |
Author:ssss |
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Description: 用于函数逼近的径向基逼近和差值，是一个基础函数，包括高斯及二项式两种，可拓展到多个应用领域-Radial basis functions are use for function approximation and interpolation. This package supports two popular classes of rbf: Gaussian and Polyharmonic Splines (of which the Thin Plate Spline is a subclass).
The package also calculates line integrals between two points. Platform: |
Size: 9216 |
Author:辛芳芳 |
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Description: RBF网络能够逼近任意的非线性函数，可以处理系统内的难以解析的规律性，具有良好的泛化能力，并有很快的学习收敛速度，已成功应用于非线性函数逼近、时间序列分析、数据分类、模式识别、信息处理、图像处理、系统建模、控制和故障诊断等。(RBF network can approximate any nonlinear function, regularity can handle within the system to parse, has good generalization ability and learning, fast convergence speed, and has been successfully applied to nonlinear function approximation, time series analysis, data classification, pattern recognition, information processing, image processing, system modeling, control and fault diagnosis.) Platform: |
Size: 5120 |
Author:gahuan
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Description: RBF网络能够逼近任意的非线性函数，可以处理系统内的难以解析的规律性，具有良好的泛化能力，并有很快的学习收敛速度，已成功应用于非线性函数逼近、时间序列分析、数据分类、模式识别、信息处理、图像处理、系统建模、控制和故障诊断等。(RBF network can approximate any nonlinear function, regularity can handle within the system to parse, has good generalization ability and learning, fast convergence speed, and has been successfully applied to nonlinear function approximation, time series analysis, data classification, pattern recognition, information processing, image processing, system modeling, control and fault diagnosis.) Platform: |
Size: 47104 |
Author:哼哼1214
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Description: RBF网络能够逼近任意的非线性函数，可以处理系统内的难以解析的规律性，具有良好的泛化能力，并有很快的学习收敛速度，已成功应用于非线性函数逼近、时间序列分析、数据分类、模式识别、信息处理、图像处理、系统建模、控制和故障诊断等。
简单说明一下为什么RBF网络学习收敛得比较快。当网络的一个或多个可调参数（权值或阈值）对任何一个输出都有影响时，这样的网络称为全局逼近网络。由于对于每次输入，网络上的每一个权值都要调整，从而导致全局逼近网络的学习速度很慢。BP网络就是一个典型的例子。(RBF network can approximate arbitrary non-linear functions, can deal with the laws that are difficult to analyse in the system, has good generalization ability, and has very fast learning.
The convergence rate has been successfully applied to non-linear function approximation, time series analysis, data classification, pattern recognition, information processing, image processing and system construction.
Modeling, control and fault diagnosis.
Simply explain why RBF network learning converges faster. When one or more adjustable parameters (weights or thresholds) of the network are applied to any output
When there is an impact, such a network is called a global approximation network. For each input, each weight on the network has to be adjusted, which leads to global approximation.
The learning speed of the network is very slow. BP network is a typical example.
If only a few connection weights affect the output for a local area of the input space,) Platform: |
Size: 2573312 |
Author:shunzi1999 |
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