Description: RBF模拟神经网络（主要用于函数拟合与模式分类）Matlab的示例程序-RBF simulated neural network (the main function for the fitting and pattern classification) Matlab sample program Platform: |
Size: 1024 |
Author:胡维刚 |
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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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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: 用rbf神经网络实现分类和曲线拟合，包括分离器和曲线拟合两个文件，可以直接解压缩使用-using neural network classification and curve fitting, including separator and curve fitting two documents, decompression can be used directly Platform: |
Size: 1024 |
Author:孟庆 |
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Description: 一个用于MRI和CT图像检索的程序，使用了SVM分类算法和AdaptBoost自适应增强算法。-for an MRI and CT image retrieval procedures, the use of SVM classification algorithm and AdaptBoost adaptive enhancement algorithms. Platform: |
Size: 15691776 |
Author:chenxin |
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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: 模糊神经网络逼近与分类，模糊规则提取，快速增长与删减网络。-Fuzzy neural network approximation and classification, fuzzy rule extraction, with the deletion of the rapid growth of the network. Platform: |
Size: 3072 |
Author:王宁 |
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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: 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: 该文件主要是在Matlab条件下开发的RBF算法，主要用于机器视觉学习分类(This file is mainly a RBF algorithm developed under the condition of Matlab, which is mainly used for machine vision learning classification.) Platform: |
Size: 26624 |
Author:ywqh
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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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Search - rbf classification