Description: 利用MATLAB对神经网络进行编程,用newff()创建两层前向网络。网络输入范围[-1 1]，第一层有10个tansig神经元-using MATLAB right neural network programming with newff () to the creation of a two-tier network. Network input range [-1 1], the first layer 10 tansig neurons Platform: |
Size: 5120 |
Author:龙海侠 |
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Description: 这是一个用matlab实现的RBF神经网络手写数字识别算法.此算法加入相应的手写数字图后可以运行.-This is a realization of using Matlab RBF neural network recognition algorithm handwritten figures. This algorithm adherence to the corresponding figures handwritten map after the run. Platform: |
Size: 2048 |
Author:陈华 |
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Description: 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 is included-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 five kinds of neural networks : a) Adaptive Linear Network (ADALINE) 2) Multilayer Layer 102206 with Extended Backpropagation algorithm (EBPA) 3) Radial Basis Functions (RBF) Networks, 4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN) 5) and RBF Networks with Piecewise Linear Dynamic Cell Structure (DCS) algorithm A Simulink example regarding the approximation of a scalar nonlinear function of four variables is included Platform: |
Size: 198656 |
Author:叶建槐 |
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Description: RBF-NN,径向基神经网络。已通过了一些列验证，应该好使。-RBF-NN, RBF neural network. Has adopted a number of out verification, it should be so. Platform: |
Size: 5120 |
Author:王明 |
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Description: rbf神经网络在变压器故障诊断中的应用，程序完整，希望大家能够用的上~-rbf neural network in the transformer fault diagnosis, procedural integrity, and hope that we can use the ~ Platform: |
Size: 1024 |
Author:魔夏 |
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Description: RBF神经网络是一种具有单隐层的三层前馈网络，输入层、隐含层和输出层。本代码主要用来建立一个RBF神经网络用进行训练。-RBF neural network used to set up Platform: |
Size: 1024 |
Author:maylene |
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Description: 介绍如何通过matlab使用bp神经网络和rbf神经网络来逼近非线性函数-Describes how to use matlab bp neural network and rbf neural networks to approximate nonlinear functions Platform: |
Size: 1024 |
Author:仁杰 |
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Description: RBf神经网络算法的具体讲解，包括各种例子，内附程序文件，很容易理解。-RBf neural network algorithm specific explanations, including examples, containing the program file, it is easy to understand. Platform: |
Size: 101376 |
Author:wangyuanyuan |
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Description: 非常好的RBF神经网络学习材料，包括神经网络的原理和matlab代码说明，很详细适合初学者。-A very good RBF neural network learning materials, including the principle of the neural network and matlab code instructions, in great detail for beginners. Platform: |
Size: 339968 |
Author:jelly |
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Description: 用四元十字阵做被动声定位算法设计，现在是用matlab神经网络工具箱构建RBF神经网络然后仿真显示图形-With a four-element Array do passive acoustic localization algorithm design, now using matlab neural network toolbox and then build on RBF neural network simulation display graphics Platform: |
Size: 7168 |
Author:张紫梦 |
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Description: 构建RBF神经网络，并显示出对应的高斯函数，仿真程序可用，运行平台为matlab-Construction of RBF neural network, and shows the corresponding Gauss function, the simulation program is available for the operating platform matlab Platform: |
Size: 1024 |
Author:戚川 |
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Description: 自己编写RBF神经网络程序，RBF神经网络隐层采用标准Gaussian径向基函数，输出层采用线性激活函数，其中数据中心、扩展常数和输出权值均用梯度法求解，它们的学习率均为0.001。其中隐节点数选为10，初始输出权值取[-0.1，0.1]内的随机值，初始数据中心取[-1，1]内的随机值，初始扩展常数取[0.1，0.3]内的随机值，输入采用[0 1]的随机阶跃输入(Write your own RBF neural network, RBF neural network hidden layer using standard Gaussian radial basis function, the output layer using a linear activation function, the data center, and the output weights are constant expansion with the gradient method, the learning rate is 0.001. The number of hidden nodes is 10, the initial output value [-0.1, random value of 0.1], initial data center [-1, random value of 1], the initial expansion constant of [0.1, a random value within 0.3], using a random order [0 1] step input) Platform: |
Size: 19456 |
Author:RED2AWN
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