Description:
RBF神经网络用于分类与回归
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作者：陆振波，海军工程大学
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个人主页：luzhenbo.88uu.com.cn
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文件说明：
1、NeuralNetwork_RBF_Classification.m - 分类
2、NeuralNetwork_RBF_Regression.m - 回归
-Neural Network for Classification and Regression---------------------------------------- Author : Lu Zhen-bo, the Navy Engineering from the University of peer welcome exchanges and cooperation, more and download articles please visit my personal web page e-mail : luzhenbo@sina.com WEBSITE : luzhenbo.88uu.com.cn---------------------------------------- documents : one, NeuralNetwork_RBF_Classification.m-2 classification, NeuralNetwork_RBF_Regression. m-reunification Platform: |
Size: 2048 |
Author:陆振波 |
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Description: 由于BP网络的权值优化是一个无约束优化问题，而且权值要采用实数编码，所以直接利用Matlab遗传算法工具箱。以下贴出的代码是为一个19输入变量，1个输出变量情况下的非线性回归而设计的，如果要应用于其它情况，只需改动编解码函数即可。程序需要调用gaot工具箱.-As a result of BP network weights optimization is a constrained optimization problems, and weights to be used real-coded, so the direct use of Matlab genetic algorithm toolbox. Posted the following code is for a 19 input variables, an output variable in case of non-linear regression designed, if applied to other situations, simply change your codec function. Procedures need to call gaot toolbox. Platform: |
Size: 4096 |
Author: |
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Description: 神经网络Office插件NNX使用说明
本软件是针对使用神经网络中最烦琐的数据和图形表示而开发，特别方便用户处理数据和编辑图形-Office plug-NNX neural network for use of this software is for the use of neural networks in the most cumbersome of data and graphical representation of the development, particularly user-friendly data processing and editing graphics Platform: |
Size: 442368 |
Author:徐寅 |
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Description: RBF神经网络matlab代码可以用于分类与回归-RBF neural network matlab code can be used for classification and regression Platform: |
Size: 3072 |
Author:贺宏洲 |
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Description: 统计模式识别、线性或非线性回归以及人工神经网络等方法是数据挖掘的有效工具，支持向量分类（support vector classification，简称SVC）算法是一个很有发展前景的方向。-Statistical pattern recognition, linear or nonlinear regression and artificial neural network approach is an effective tool for data mining, support vector classification (support vector classification, referred to as SVC) algorithm is a promising direction. Platform: |
Size: 10240 |
Author:xs |
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Description: 本人编写的incremental 随机神经元网络算法，该算法最大的特点是可以保证approximation特性，而且速度快效果不错，可以作为学术上的比较和分析。目前只适合benchmark的regression问题。
具体效果可参考
G.-B. Huang, L. Chen and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes”, IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879-892, 2006.
-I prepared by incremental random neural network algorithm, which is characterized by the largest approximation properties can be guaranteed, and fast good results can be used as an academic comparison and analysis. The current benchmark is only suitable for the regression problem. Specific effects may refer G.-B. Huang, L. Chen and C.-K. Siew, Platform: |
Size: 2048 |
Author:chenlei |
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Description: BP网络的应用： BP神经网络用于分类与回归， 使用matlab打开-Application of BP Network: BP neural network for classification and regression, the use of matlab to open Platform: |
Size: 3072 |
Author:ycs |
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Description: 广义回归神经网络是RBF的一个扩展。但是具体怎么实现却代码很少，本代码是一个grnn很好的学习例子。-Generalized regression neural network is an extension of RBF. But how to achieve specific code but rarely, the code is a good learning example grnn. Platform: |
Size: 1024 |
Author:王淑娟 |
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Description: 基于多元线性回归、偏最小二乘、神经网络、卡尔漫滤波、径向基网络、主成分分析等等的程序。可用于建模和预测。-Based on multiple linear regression, partial least squares, neural networks, Carl diffuse filtering, radial basis networks, and so on principal component analysis procedure. Can be used for modeling and prediction. Platform: |
Size: 24576 |
Author:yinjj |
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Description: The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local memory-based models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.-The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local memory-based models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
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Size: 2140160 |
Author:Tthias |
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Description: RBF神经网络用于分类与回归，非常实用，强烈建议下载-RBF neural network for classification and regression, very practical, it is strongly recommended to download Platform: |
Size: 2048 |
Author:leo |
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Search - neural regression