Description: 鲁棒控制器设计，由于RBF网络可以实现任意逼近的非线性关系，它的目标是要做到误差平方和最小，与非线性PCA的目标一致，所以上述非线性PCA的模型可以通过采用两个RBF网络来实现非线性正变换 和反变换 。RBF网络是一个三层前馈网络，隐层采用径向基函数作为激励函数。第一个RBF网络把高维空间的数据映射到低维空间（如图4），第二个RBF网络将前面网络输出的低维空间数据再映射到高维空间，实现数据恢复(如图5)。这两个网络分别进行训练。-robust controller design, as RBF networks can achieve arbitrary nonlinear approximation, Its goal is to achieve the minimum squared error, and nonlinear PCA have the same goal So these nonlinear PCA model may be adopted by two RBF networks to achieve nonlinear transformation and inverse transform. RBF network is a feed-forward network, hidden layer RBF function as an incentive. RBF a network of high-dimensional data mapping space to the low-dimensional space (figure 4), second RBF network will be in front of the output of low-dimensional space mapping data again to a high-dimensional space. data Recovery (figure 5). The two networks separately for training. Platform: |
Size: 1024 |
Author:浇洒距离 |
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Description: bp神经网络算法是解决最优化问题的先进算法之一，本论文讨论了神经网络中使用最为广泛的前馈神经网络。其网络权值学习算法中影响最大的就是误差反向传播算法（back-propagation简称BP算法）。BP算法存在局部极小点，收敛速度慢等缺点。基于优化理论的Levenberg-Marquardt算法忽略了二阶项。该文讨论当误差不为零或者不为线性函数即二阶项S(W)不能忽略时的Hesse矩阵的近似计算，进而训练网络。-bp neural network algorithm to solve optimization problems, one of the advanced algorithm, the paper discusses the neural network in the most widely used feed-forward neural network. Its network weights learning algorithm in the greatest impact is the error back-propagation algorithm (back-propagation algorithm referred to as BP). BP algorithm for the existence of local minimum points, such as the shortcomings of slow convergence. Optimization theory based on the Levenberg-Marquardt algorithm ignores the second-order item. In this paper, the discussion when the error is not zero or not that is second-order linear function of S (W) can not be ignored when the Hesse matrix of approximate calculation, and then training the network. Platform: |
Size: 19456 |
Author:刘慧 |
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Description: 三层前馈神经网络的BP算法。程序具有以下功能：
（1） 允许选择各层节点数；
（2） 允许选用不同的学习率η；
（3） 能对权值进行初始化，初始化用[-1、1]区间的随机数；
（4）允许选用单极性和双极性两种不同Sigmoid型转移函数。
-Three-tier feed-forward neural network BP algorithm. Procedures have the following functions: (1) allows to choose the number of nodes on each floor (2) allows selection of different learning rate η (3) be able to initialize the weights, initialized by [-1,1] interval random number (4) to allow selection of unipolar and bipolar type two different Sigmoid transfer function. Platform: |
Size: 1024 |
Author:Mingruixia |
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Description: matlab动量梯度下降算法
生成一个新的前向神经网络
对BP神经网络进行训练
对BP神经网络进行仿真-Momentum matlab gradient descent algorithm to generate a new feed-forward neural networks trained BP neural network on the BP neural network simulation Platform: |
Size: 1024 |
Author:舒 |
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Description: RNNSIM ver. 1.0 is a program with an intercative graphical user interface
(GUI) that runs under MATLAB ver. 5.0 or higher. The program can be used
in training and testing the Random Neural Network(RNN) models.
This version (ver. 1.0) implements only the 3 layer feed forward RNN model.
In the next versions, the multi hidden layers and the recurrent RNN models
can be implemented. To obtain faster training, the training section can be
written as a MEX file and invoked from the GUI.
If you have the m files in the directory rnnsim for example, then you can
run the program following the next steps:
1- run MATLAB as usual
2- from the MATLAB command window, write cd rnnsim
3- from the MATLAB command window, write rnnsim- RNNSIM ver. 1.0 is a program with an intercative graphical user interface
(GUI) that runs under MATLAB ver. 5.0 or higher. The program can be used
in training and testing the Random Neural Network(RNN) models.
This version (ver. 1.0) implements only the 3 layer feed forward RNN model.
In the next versions, the multi hidden layers and the recurrent RNN models
can be implemented. To obtain faster training, the training section can be
written as a MEX file and invoked from the GUI.
If you have the m files in the directory rnnsim for example, then you can
run the program following the next steps:
1- run MATLAB as usual
2- from the MATLAB command window, write cd rnnsim
3- from the MATLAB command window, write rnnsim
Platform: |
Size: 63488 |
Author:hacen |
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Description: 针对各种机器学习，深度学习领域的一个matlab工具包-A machine learning library focused on deep learning.Following algorithms and models are provided along with some static utility classes:
- Naive Bayes, Linear Regression, Logistic Regression, Softmax Regression, Linear Support Vector Machine, Non-Linear Support Vector Machine (with RBF kernel),
Feed-forward Neural Network, Embedding Neural Network, Convolutional Neural Network, Sparse Autoencoders, Denoising Autoencoders,
Contractive Autoencoders, Stacked Sparse Autoencoders, Self-Taught Learner and Restricted Boltzmann Machines are tested with this version.
- Rest of the methods are not tested hence not supplied and the progress is as follows:
+ Deep Belief Nets with Restricted Boltzmann Machines (not tested)
+ Bayes Nets (tested- refactoring)
+ Hidden Markov Models (tested- refactoring)
+ Conditional Random Fields (work in progress) Platform: |
Size: 346112 |
Author:zhjhe |
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Description: Using MATLAB tools for MLP NNs (e.g., newff, …), design a two-layer feed-forward neural network as a classifier to categorize the input geometric shapes.
- The snapshot and bitmap of shapes are given:
- Training shapes: shkt.bmp
- Training patterns: trn.txt (each shape is in a 125*140 matrix)
- Test shapes: shks.bmp
- Test patterns: tsn.txt (each shape is in a 125*140 matrix)
- Since the dimension of inputs is too high (17500-dimensional), it is not possible to apply them directly to the net. So, … .
- Try the number of hidden neurons to be at least.
- Do training of NN until all training patterns are truly classified.
- To examine the generalization ability of your NN after training,
a) Apply it to the test patterns and report the accuracies.
b) Add p noise (p=5, 10, …, 75) to the training shapes (only degrade the
black pixels of the shapes) and report in a plot the accuracy versus p.-Using MATLAB tools for MLP NNs (e.g., newff, …), design a two-layer feed-forward neural network as a classifier to categorize the input geometric shapes.
- The snapshot and bitmap of shapes are given:
- Training shapes: shkt.bmp
- Training patterns: trn.txt (each shape is in a 125*140 matrix)
- Test shapes: shks.bmp
- Test patterns: tsn.txt (each shape is in a 125*140 matrix)
- Since the dimension of inputs is too high (17500-dimensional), it is not possible to apply them directly to the net. So, … .
- Try the number of hidden neurons to be at least.
- Do training of NN until all training patterns are truly classified.
- To examine the generalization ability of your NN after training,
a) Apply it to the test patterns and report the accuracies.
b) Add p noise (p=5, 10, …, 75) to the training shapes (only degrade the
black pixels of the shapes) and report in a plot the accuracy versus p. Platform: |
Size: 3072 |
Author:fatemeh |
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Description: The following Matlab project contains the source code and Matlab examples used for gradient neural network. The form of a single layer feed forward neural network lends itself to finding the gradient.
The source code and files included in this project are listed in the project files section, please make sure whether the listed source code meet your needs there.-The following Matlab project contains the source code and Matlab examples used for gradient neural network. The form of a single layer feed forward neural network lends itself to finding the gradient.
The source code and files included in this project are listed in the project files section, please make sure whether the listed source code meet your needs there.
Platform: |
Size: 44032 |
Author:sina |
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Description: The following Matlab project contains the source code and Matlab examples used for neural network programs. Perceptron LMS Feed Forward Back Propagation Character Recognition
The source code and files included in this project are listed in the project files section, please make sure whether the listed source code meet your needs there. Platform: |
Size: 13312 |
Author:sina |
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Search - feed forward network in matlab