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Description: 可以用C++语言开发各种神经网络：BP，RBF，HOP~…………，使用前请看看说明文档，然后建立一个自己的项目文件，只要能明白作者的思路就能很方便地进行各种神经网络的设计，本人现在就在一个系统中使用它，目前这个开发包的版本已进入0.7了，但功能差不多，本人认为0.5这个版本用着也很方便，所以把他发来，大家共享。-C language can be used to develop a variety of neural networks : BP, RBF, HOP ~ ... ... please look at the use of pre-documented, and then the establishment of a project document, you will be able to understand the author's ideas will be very convenient for the various neural network design, I in a system in which to use it, the current version of the development kit has entered 0.7, but the function almost, I think that this version of 0.5 is also very convenient, so he made to share.
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Author: 李洋 |
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Description: 自适应(Adaptive)神经网络源程序
The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring
different adaptation algorithms.~..~
There are 11 blocks that implement basically these 5 kinds of neural networks:
1) Adaptive Linear Network (ADALINE)
2) Multilayer Layer Perceptron with Extended Backpropagation algorithm (EBPA)
3) Radial Basis Functions (RBF) Networks
4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN)
5) RBF and Piecewise Linear Networks with Dynamic Cell Structure (DCS) algorithm
A simulink example regarding the approximation of a scalar nonlinear function of 4 variables -Adaptive (Adaptive) The neural network source adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring different adaptation algorithms .~..~ There are 11 blocks that implement basically these five kinds of neural networks : a) Adaptive Linear Network (ADALINE) 2) 102206 with Multilayer Layer Extended Backpropagation algorithm (EBPA) 3) Radial Basis Functions (RBF) Networks, 4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN) 5) RBF and Piecewise Linear Dynamic Networks with the Cell Structure (DCS) algorithm A Simulink example regarding the approximation of a scalar nonlinear function of four variables
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Author: 周志连 |
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Description: SVM用于模式识别
整理别人的代码（原来的是英文）所得：
kernel.m用于内积矩阵的计算
svcplot.m用于绘图
svm168.m是主程序
testlin.m是采用线形内积函数的支持向量机应用的 文件
testrbf.m是采用RBF内积函数的支持向量机应用 的 文件
每个文件中都有说明。
仿真平台matlab7.0, 仿真全部通过
将所有文件拷贝到work目录下（注意不要直接将上层文件夹直接拷贝到work目录下，若那样操作，必须在matlab的file菜单中的set path中设置相应的路径）。
打开matlab，在命令窗口中输入 testlin或testrbf 即可查看结果。
-SVM pattern recognition for collating other people's code (the original is in English) from : kernel.m plot within the matrix for the calculation svcplot.m for graphics is the main svm168.m testlin.m procedure is used linear plot function within the SVM application documents testrbf.m RBF is using plot function within the SVM applications each file documents were described. Matlab7.0 simulation platform, all through the simulation of all the documents copied to the work directory (not directly to the attention of the upper folder directly copied to w contex directory, as if the operation, Matlab in the file menu on the set path corresponding set the path). Open Matlab, in the command window or imported testlin testrbf can see the results.
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Author: 郑军 |
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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.
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Author: 陈伟 |
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Description: 使用matlab编写的蚁群算法程序，供大家参考使用。-Matlab using ant colony algorithm to prepare procedures for U.S. use and reference.
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Size: 1024 |
Author: 张杰 |
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Description: 因其核函数的良好性态,RBF核SVM(RBF-SVM)在实际应用中表现出良好的学习性能,但是RBF核函数中的参数对SVM的性能起决定性作用.阐述了RBF-SVM的性能随着变化而变化的规律,并将RBF-SVM引入自动羽绒识别系统中.根据自动羽绒识别系统的实际需求和RBF-SVM的性能变化规律,论述了本系统中参数的选取依据和选取过程,并且给出了的相关曲线变化图.通过研究,最后得到适合本系统的识别模型,从而提高了系统的总体识别率.同时,也验证了RBF-SVM的良好特性和其受参数的约束规律.
-err
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Size: 7168 |
Author: 苏苏 |
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Description: RBF神经网络的混合学习法，RBF现在越来越受到大家的青睐-RBF neural network hybrid learning method, RBF is increasingly favored by the U.S.
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Size: 178176 |
Author: 张聪誉 |
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Description: 模糊滑模神经网络RBF在倒立摆系统的应用-Fuzzy sliding-mode neural network RBF at the inverted pendulum system
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Size: 1024 |
Author: zhaominghui |
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Description: 基本遗传算法,RBF神经网络算法,BP神经网络算法,一共5个源代码.程序可移植性强.-Basic genetic algorithm, RBF neural network algorithm, BP neural network algorithm, a total of 5 source code. Program portability strong.(
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Size: 10240 |
Author: 王永超 |
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Description: RBF程序及GUI仿真，希望对大家有用，不知道对不对，请大家指点。-RBF procedures and GUI emulation, everyone would like to be useful, I do not know right, please instruct the U.S..
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Size: 6144 |
Author: yanghaiyan |
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Description: RBF神经网络用s函数编程的，主要功能是用作控制器-RBF neural network with programming function s main function is used as a controller
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Size: 1024 |
Author: feiyu |
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Description: RBF源代码 我调试过的 用S函数编写的 在simulink里 一切都弄好的 希望对大家有帮助-the code of RBF
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Author: caoying |
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Description: Three function for RBF neural network, using OLS,Rand and SGA
function [newcenter,sigma,W,yh,rmse]=rbfols(p,t,tol)
p 為輸入資料點，N×K矩陣，N是輸入資料維度，K是資料點數
t 為目標輸出值，1×K矩陣
tol 為指定容忍度或正確率
centers選定中心點，N×nc矩陣
sigma為 ? 值
W為輸出層權重，nc×1矩陣
yh為網路輸出值，1×K矩陣
rmse 為目標輸出值與網路輸出值之RMSE-Three function for RBF neural network, using OLS,Rand and SGA
function [newcenter,sigma,W,yh,rmse]=rbfols(p,t,tol)
p 為輸入資料點，N×K矩陣，N是輸入資料維度，K是資料點數
t 為目標輸出值，1×K矩陣
tol 為指定容忍度或正確率
centers選定中心點，N×nc矩陣
sigma為 ? 值
W為輸出層權重，nc×1矩陣
yh為網路輸出值，1×K矩陣
rmse 為目標輸出值與網路輸出值之RMSE
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Size: 2048 |
Author: aaronwu |
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Description: 基于PSO算法的故障诊断分析-简单算例
script file:gzzd1_PSO.m
找出最能解释警报信号的故障假说
目标函数E(s),4维函数-PSO algorithm based fault diagnosis- A simple example script file: gzzd1_PSO.m find the best hypothesis to explain the failure warning signal objective function E (s), 4-dimensional function
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Size: 2048 |
Author: 李智 |
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Description: -s svm类型：SVM设置类型(默认0)
0 -- C-SVC
1 --v-SVC
2 – 一类SVM
3 -- e -SVR
4 -- v-SVR
-t 核函数类型：核函数设置类型(默认2)
0 – 线性：u v
1 – 多项式：(r*u v + coef0)^degree
2 – RBF函数：exp(-r|u-v|^2)
3 –sigmoid：tanh(r*u v + coef0)
-d degree：核函数中的degree设置(针对多项式核函数)(默认3)
-g r(gama)：核函数中的gamma函数设置(针对多项式/rbf/sigmoid核函数)(默认1/ k)
-r coef0：核函数中的coef0设置(针对多项式/sigmoid核函数)((默认0)
-c cost：设置C-SVC，e -SVR和v-SVR的参数(损失函数)(默认1)
-n nu：设置v-SVC，一类SVM和v- SVR的参数(默认0.5)
-p p：设置e -SVR 中损失函数p的值(默认0.1)
-m cachesize：设置cache内存大小，以MB为单位(默认40)
-e eps：设置允许的终止判据(默认0.001)
-h shrinking：是否使用启发式，0或1(默认1)
-wi weight：设置第几类的参数C为weight*C(C-SVC中的C)(默认1)
-v n: n-fold交互检验模式，n为fold的个数，必须大于等于2--s svm_type : set type of SVM (default 0)
0-- C-SVC
1-- nu-SVC
2-- one-class SVM
3-- epsilon-SVR
4-- nu-SVR
-t kernel_type : set type of kernel function (default 2)
0-- linear: u *v
1-- polynomial: (gamma*u *v+ coef0)^degree
2-- radial basis function: exp(-gamma*|u-v|^2)
3-- sigmoid: tanh(gamma*u *v+ coef0)
4-- precomputed kernel (kernel values in training_instance_matrix)
-d degree : set degree in kernel function (default 3)
-g gamma : set gamma in kernel function (default 1/k)
-r coef0 : set coef0 in kernel function (default 0)
-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
-m cachesize : set cache memory size in MB (default 100)
-e epsilon : set tolerance of termination criterion (default 0.001)
-h shrinking: whether to use the shrinking heuristics, 0 or 1 (default 1)
-b
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Author: little863 |
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Description: 对数据的预测额，很好用的模型，下载别人的。(Predict the amount of data, use the model well, download someone else's)
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Size: 2048 |
Author: wdbwww
|
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Description: RBF网络的高斯基函数及逼近器S函数设计仿真(Gaussian Function and Approximator S Function Design and Simulation of RBF Network)
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Author: hunterFM |
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Description: 完成RBF系统辨识，对模型进行辨识，得到预测的输出值，这里用word形式把代码粘贴在里面了，自己手敲的，亲测好用(Complete the RBF system identification, identify the model and get the predicted output value. Here, the code is pasted in the form of word. It's easy to use by hand.)
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Size: 10240 |
Author: 周颖 |
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Description: 刘金琨老师的著作，主要讲RBF在控制方面的应用，章节末附有源程序(Liu Jinkun teacher's work, mainly about the application of RBF in control, chapter end with the active program)
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Size: 51730432 |
Author: 醉里挑灯看雪 |
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