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: |
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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: 基于梯度法编写的RBF神经网络程序，实现对输入数据的逼近-Gradient method based on the preparation process of the RBF neural network to achieve the approximation of the input data Platform: |
Size: 1024 |
Author:wshli |
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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.
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Size: 10240 |
Author:ssss |
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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网络的逼近器对连续系统进行逼近的仿真程序(Approximation Simulation Program for Continuous Systems by Approximators of RBF Networks) Platform: |
Size: 1024 |
Author:hunterFM |
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