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matlab实现 感应器神经网络、线性网络、BP神经网络、径向基函数网络-matlab neural network implementation of sensors, linear network, BP neural networks, Radial Basis Function Network
Update : 2025-02-19 Size : 3kb Publisher : xiuzhuo

DL : 0
RBF的MATLAB M文件实现。不是MATLAB的工具箱函数。-RBF the realization of the MATLAB M files. MATLAB is not a function of the toolbox.
Update : 2025-02-19 Size : 1kb Publisher : kendor

DL : 0
基于聚类的径向基神经网络算法,matlab实现-Cluster-based radial basis function neural network algorithm, matlab implementation
Update : 2025-02-19 Size : 4kb Publisher : axun

DL : 0
径向基函数RBF神经网络的Matlab实现代码-Radial basis function RBF neural network Matlab implementation code
Update : 2025-02-19 Size : 1kb Publisher : hgdzkl

DL : 0
自适应神经网络图书馆(Matlab的5.3.1或更高版本)是实现多个自适应神经网络具有不同的自适应算法块的集合。 它主要分布于2001年6月 - 7月詹皮耶罗坎帕(西弗吉尼亚大学)和马里奥·卢卡Fravolini(佩鲁贾大学)。后来改善部分由美国航天局资助NCC5-685支持。 有迹象表明,实施基本上这些种神经网络的块: 自适应线性网络(ADALINE) 多层感知层网络 广义径向基函数网络 动力单元结构(DCS)网络的高斯或圆锥形基函数 此外,包括有关的标量非线性函数逼近的一个Simulink的例子。 最后,该文件包括Training.zip步步instrucions上如何培养GRBF网络和支撑例子。-Adaptive Neural Network Library (version 5.3.1 or higher Matlab' s) is to achieve a plurality of adaptive neural network has a different set of adaptive algorithms block. It is mainly distributed in June 2001- July 詹皮耶罗坎 Pa (West Virginia University) and Mario Luca Fravolini (Perugia University). Later improved in part by the NASA-funded NCC5-685 support. There are indications that the implementation of these types of neural networks basically block: adaptive linear network (ADALINE) multi-layer network-aware generalized radial basis function network power unit structure (DCS) network Gaussian or conical base function addition, including related Examples of scalar nonlinear function approximation of a Simulink. Finally, the document includes a step by step instrucions on how to cultivate GRBF Training.zip network and support examples.
Update : 2025-02-19 Size : 584kb Publisher : YuWang

RBF神经网络的matlab实现,简单易懂(RBF neural network matlab implementation, easy to understand)
Update : 2025-02-19 Size : 13kb Publisher : lubyn

SVM Light工具箱 Matlab接口,已经编译好,可直接用(SVMlight, by Joachims, is one of the most widely used SVM classification and regression package. It has a fast optimization algorithm, can be applied to very large datasets, and has a very efficient implementation of the leave-one-out cross-validation. Distributed as C++ source and binaries for Linux, Windows, Cygwin, and Solaris. Kernels: polynomial, radial basis function, and neural (tanh).)
Update : 2025-02-19 Size : 65kb Publisher : ym89413
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