搜索资源列表
neural-network-
- 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
Radial-Basis-Function-Network
- 径向基函数网络,径向基函数网络的基础，内容清楚，值得借鉴-Radial basis function network based radial basis function network, the content is clear, it is worth learning
CHENGXU2
- 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
RBF
- 使用RBF（径向基函数）神经网络进行函数拟合-Function fitting using RBF (radial basis function) neural network
DVMS_V4.0
- rbf神经网络例代码，供参考, RBF网络的结构与多层前向网络类似，它是一种三层前向网络。输入层由信号源结点组成；第二层为隐含层，隐单元数视所描述问题的需要而定，隐单元的变换函数是RBF径向基函数-RBF neural network example code, for your reference
RBF
- 一款基于MFC的模式识别小软件，算法为径向基函数神经网络（RBF）-A small algorithm based on MFC for pattern recognition, the algorithm is a radial basis function neural network (RBF)
MATLAB神经网络原理与实例精解
- 本书简要介绍MATLAB软件的使用和常用的内置函数，随后分门别类地介绍了BP神经网络、自组织网络、反馈网络等不同类型的神经网络，并在每章的最后给出了实例。(This book introduces the use of MATLAB software and commonly used built-in functions, then be arranged to introduces the BP neural network, self-organizing network, feedbac
MPS
- 使用用径向基函数特解方法求解偏微分方程，这是一种转化的思想，只适合特定的方程(The partial differential equation is solved by the MPS of radial basis function)
rbfinterp
- 利用径向基函数进行函数插值，例子中包括一维和二维的情况，尤其是二维，生成插值的时候尽量不用双循环(Using radial basis function interpolation, examples include one-dimensional and two-dimensional, especially two-dimensional, when interpolation, as far as possible without double loop)
thin plate spline
- 利用径向基函数中经典的薄板样条方法进行数值仿真，程序的难点在于薄板样条基函数的构造(The numerical simulation of the classical thin plate spline method in radial basis function is presented. The difficulty of the program is the construction of the thin plate spline basis functions)
三RBF (1)
- 高斯径向基函数神经网络RBFmatlab代码，提高非线性输入处理能力(Gauss radial basis function neural network RBFmatlab code to improve nonlinear input processing capability)
RBF
- 径向基函数 (Radial Basis Function 简称 RBF)，最常用的径向基函数是高斯核函数(RBF(Radial Basis Function))
rbf
- 自己编写RBF神经网络程序，RBF神经网络隐层采用标准Gaussian径向基函数，输出层采用线性激活函数，其中数据中心、扩展常数和输出权值均用梯度法求解，它们的学习率均为0.001。其中隐节点数选为10，初始输出权值取[-0.1，0.1]内的随机值，初始数据中心取[-1，1]内的随机值，初始扩展常数取[0.1，0.3]内的随机值，输入采用[0 1]的随机阶跃输入(Write your own RBF neural network, RBF neural network hidden layer
jingxiang
- 严格(Exact)径向基网络来实现非线性的函数回归(Nonlinear function regression for strict (Exact) radial basis function networks)
svm
- 支持向量机由Vapnik首先提出，像多层感知器网络和径向基函数网络一样，支持向量机可用于模式分类和非线性回归，该程序主要实现svm的分类和回归功能。(SVM was first proposed by Vapnik. Like multilayer sensor network and radial basis function network, SVM can be used for pattern classification and non-linear regression. The p
rbfPID
- 基于RBF径向基函数神经网络的PID在线整定，里边的目标函数可自行修改(Based on RBF Radial Basis Function Neural Network PID online tuning, the inside of the objective function can be modified)
BP,RBF
- BP神经网络作为一种前馈性的神经网络，RBF神经网络由于其独特的联想记忆功能，常常用来用于识别和优化计算方问题上。分别对这两种算法用于对逼近非线性函数进行编程，观察其拟合情况后，用其他未训练的样本数据进行泛化能力分析。(BP neural network is a feed-forward neural network. RBF neural network is often used to identify and optimize the computation problem due to
matlab_neural etwork
- 感应器神经网络、线性网络、BP神经网络、径向基函数网络(Sensor neural network, linear network, BP neural network, radial basis function network)
rbf
- 该工具箱提高了径向基函数的matlab编码，能够实现函数拟合，用于黑箱问题优化(Approximation and optimizaiton for black-box problems)
python-codes
- scipy_uspline.py: 使用UnivariateSpline进行插值、外推和Spline拟合; scipy_stats.py: 演示stats模块的概率密度函数、直方图统计和累积分布函数。 scipy_rbf.py: 演示径向基函数(radial basis function, 简称RBF)插值算法。 scipy_fsolve.py: 使用fsolve()计算非线性方程组的解。 numpy_polyfit.py:使用多项式函数拟合正弦波，并显示拟合误差。(scipy_uspl