Description: SOM神经网络，可以进行特征提取和模式分类，特别是特征维数较多的情况。-SOM neural network, can feature extraction and classification, in particular characteristic dimension of more. Platform: |
Size: 1024 |
Author:周刚 |
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Description: 在用混沌理论和神经网络进行短期负荷预测时，神经网络的输入的选择至关重要，该程序用matlabl实现了基于混沌时间序列的嵌入维数的选择-using chaos theory and neural networks for short-term load forecasts, the neural network is essential to choose an input, The procedure used matlabl achieved a chaotic time series based on the embedding dimension of choice Platform: |
Size: 1024 |
Author:sunyan |
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Description: This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.-This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar-xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. Platform: |
Size: 220160 |
Author:晨间 |
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Description: 对一个50个结点（更多的节点的网络只需要修改模块中的标量维数就行）的复杂非线性耦合网络进行同步化仿真。首先生成K矩阵，然后运行simulink，即可得到50个洛仑兹混沌节点复杂网络的同步化曲线。-Of a 50-node (more network nodes only need to modify module scalar dimension on the line) the complexity of nonlinear coupling network synchronization simulation. First Generation K matrix, and then run the simulink, can be chaotic 50 Lorentz complex network node synchronization curve. Platform: |
Size: 9216 |
Author:zhongsir |
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Description: 提出一种基于分形理论和BP 神经网络的航空遥感图像有监督分类方法。该方法尝试将航空图像
的光谱信息和纹理特征相结合。它首先将彩色航空图像由RGB 格式转化为HSI 格式,然后,根据亮度计算分
数维、多重分形广义维数谱q-D( q) 和“空隙”等基于分形的纹理特征,同时加入归一化的色度和饱和度作为光
谱特征,采用BP 神经网络作为分类器。通过对彩色航空图像的分类实验,结果证实该方法行之有效。-Based on fractal theory and BP neural network of aviation remote sensing image supervised classification method. This method tries to aerial images of the spectral information and texture characteristics of the combination. It will first color aerial images from the RGB format into HSI format, and then, according to the brightness calculation of fractal dimension, the generalized multi-fractal dimension spectrum qD (q) and the Platform: |
Size: 274432 |
Author:xuhuoping |
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Description: 支持向量机作为统计学习理论的实现方法，能很好地解决非线性和高维数问题，克服了神经网络方法收敛慢、解不稳定、推广性差的缺点，近年来得到了广泛地研究，在模式识别、信号处理、控制、通讯等方面得到了广泛地应用。-Support Vector Machine as the implementation of statistical learning theory approach, can be a good solution to the nonlinear and high dimension problem, the neural network method to overcome the slow convergence, solution instability, bad promotion of sexual shortcomings, in recent years has been widely studied, at pattern recognition, signal processing, control, communications, etc. has been widely applied. Platform: |
Size: 2688000 |
Author:王旭 |
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Description: SOM神经网络（自组织特征映射神经网络）是一种无导师神经网路。网络的拓扑结构是由一个输入层与一个输出层构成。输入层的节点数即为输入样本的维数，其中每一节点代表输入样本中的一个分量。输出层节点排列结构是二维阵列。输入层X中的每个节点均与输出层Y每个神经元节点通过一权值(权矢量为W)相连接，这样每个输出层节点均对应于一个连接权矢量。
自组织特征映射的基本原理是，当某类模式输入时，其输出层某一节点得到最大刺激而获胜，获胜节点周围的一些节点因侧向作用也受到较大刺激。这时网络进行一次学习操作，获胜节点及其周围节点的连接权矢量向输入模式的方向作相应的修正。当输入模式类别发生变化时，二维平面上的获胜节点也从原来节点转移到其它节点。这样，网络通过自组织方式用大量训练样本数据来调整网络的连接权值，最后使得网络输出层特征图能够反映样本数据的分布情况。根据SOM网络的输出状况，不仅能判断输入模式所属的类别，使输出节点代表某类模式，而且能够得到整个数据区域的分布情况，即从样本数据得到所有数据的分布特征。 -SOM neural network (self-organizing feature map neural network) is an unsupervised neural network. Network topology is an input layer and an output layer. Input layer nodes is the input dimension of the sample, each node represents a component input samples. Output layer nodes are arranged in two-dimensional array structure. X in the input layer and output layer each node of each neuron node Y by a weight (the weight vector as W) is connected, so that each output layer corresponds to a connection node of the right vector.
Self-organizing feature maps of the basic principle is, when each category of inputs into the model, its output layer one node get the maximum boost and win, Huoshengjiedian around Yixiejiedian Yin Zuo Yong Ye Shoudaojiaotai lateral stimulation. Then a learning network operation, the winner node and surrounding nodes in the right direction vector to the input mode to make consequential amendments. When the input mode type changes, the two-dimensional plane of the wi Platform: |
Size: 47104 |
Author:leidan |
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Description: 最小RMSE神经网络方法计算Lyapunov指数的matlab函数。-This M-file calculates Lyapunov exponents with minimum RMSE neural network. After estimation of network weights and finding network with minimum BIC, derivatives are calculated. Sum of logarithm of QR decomposition on Jacobian matrix for observations gives spectrum of Lyapunov Exponents. Using the code is very simple, it needs only an scalar time series, number of lags and number of hidden unites. Higher number of hidden units leads to more precise estimation of Lyapunov exponent, but it is time consuming for less powerful personal computers. Number of lags determines number of embedding dimensions. Therefore, please give number of lags equal to number of embedding dimension. The codes creates networks with various neurons up to user supplied value for neurons and lags up to user specified number lags. Total number of networks are equal to number of neurons times number of lags. this modeling strategy is complex but helps to user select embedding dimension based on minimum BIC. Platform: |
Size: 2048 |
Author:miaomiao |
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Description: 针对神经网络的瓦斯预测模型存在的泛化性能差且存在易陷入局部最优的缺点，提出了
基于最小二乘支持向量机(LS-SVM)时间序列瓦斯预测方法．由于标准最小二乘支持向量机
(L孓SVM)要求样本误差分布服从高斯分布，且标准LS-SVM丧失鲁棒性与稀疏性等特点，提出
了基于加权LS-SVM的瓦斯时间序列预测的方法，从而提高了标准L孓SVM模型的鲁棒性．其
中时间序列的嵌入维数与延迟时间采用了微熵率最小原则进行选取，在此基础上给出了基于加
权L孓SVM实现多步时间序列预测的算法实现步骤．最后利用MATLAB 7．1对其进行仿真研
究，通过鹤壁十矿1个突出工作面的瓦斯涌出数据实例对模型进行了验证．结果表明，加权
SVM模型比标准的L§SVM明显提高了鲁棒性，可较好地实现时间序列数据的多步预测．-The neural network gas prediction model is poor in generalization performance and
easy in fafling into the local optimal value．In order to overcome these shortcomings，we pro—
pose the time series gas prediction method of least squares support vector machine(L§SVM)．
However，in the LS-SVM case，the sparseness and robustness may lose，and the estimation of
the support values iS optimal only in the case of a Gaussian distribution of the error variables．
So，this paper proposes the weighted L孓SVM tO overcome these tWO drawbacks．Meanwhile，
the optimal embedding dimension and delay time of time series are obtained by the smallest dif—
ferential entropy method．On this basis，multi-step time series prediction algorithm steps are
given based on the weighted LS-SVM．Finally，the data of gas outburst in working face of Hebi
lOth mine iS adopted to validate this model．The results show that the predict effect of shortterm
the face gas emission is better using the weighted LS-SVM model than using Platform: |
Size: 490496 |
Author:wanggen |
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Description: 《MATLAB神经网络30个案例分析》中的第27个例子，案例27 遗传算法的优化计算——建模自变量降维。希望对大家有一定的帮助！-The MATLAB neural network analysis of 30 cases of 27 example, 27 cases of genetic algorithm optimization, modeling the independent variable dimension reduction. Hope to have certain help to everybody!
Platform: |
Size: 91136 |
Author:杨飞 |
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Description: 空气质量降维B-P神经网络评价法及其MATLAB实现
简介: 用主成分分析将多维空间的样本数据降维到低维空间，将其作为BP网络的输入。BP网络的算法采用LM优化法。描述了该网络的MATLAB程序语言实现过程。-Air quality dimension reduction BP neural network uation method on MATLAB Introduction: Using principal component analysis will drop the sample data in multidimensional space dimension to low-dimensional space, as the input of BP network. BP network algorithm using LM optimization method. The network describes the MATLAB programming language implementation process. Platform: |
Size: 203776 |
Author:唐小米 |
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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: 包含优化类的几个简单示例程序，信号维数的估计，基于SVPWM的三电平逆变的matlab仿真，独立成分分析算法降低原始数据噪声，复化三点Gauss-lengend公式求pi，采用了小波去噪的思想，基于人工神经网络的常用数字信号调制。-Optimization class contains several simple sample programs, Signal dimension estimates, Based on SVPWM three-level inverter matlab simulation, Independent component analysis algorithm reduces the raw data noise, Complex of three-point Gauss-lengend the Formula pi, Using wavelet denoising thought, The commonly used digital signal modulation based on artificial neural network. Platform: |
Size: 6144 |
Author:bcfcfgg |
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Description: matlab小波分析程序，是一种双隐层反向传播神经网络，用于特征降维，特征融合，相关分析等,信号维数的估计，双向PCS控制仿真，对于初学者具有参考意义，大学数值分析算法，车牌识别定位程序的部分功能。-matlab wavelet analysis program, Is a two hidden layer back propagation neural network, For feature reduction, feature fusion, correlation analysis, Signal dimension estimates, Two-way PCS control simulation, For beginners with a reference value, University of numerical analysis algorithms, Part of the license plate recognition locator feature. Platform: |
Size: 8192 |
Author:tvqusapm |
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Description: 这个有中文注释，看得明白，信号维数的估计，通过虚拟阵元进行DOA估计，课程设计时编写的matlab程序代码，三相光伏逆变并网的仿真，均值便宜跟踪的示例，现代信号处理中谱估计在matlab中的使用。- The Chinese have a comment, understand it, Signal dimension estimates, Conducted through virtual array DOA estimation, Course designed to prepare the matlab program code, Three-phase photovoltaic inverter and network simulation, Example tracking mean cheap, Modern signal processing used in the spectral estimation in matlab. Platform: |
Size: 6144 |
Author:tqxspd |
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Description: 快速扩展随机生成树算法，用于时频分析算法，从先验概率中采样，计算权重，虚拟力的无线传感网络覆盖，用MATLAB实现的压缩传感，信号维数的估计。- Rapid expansion of random spanning tree algorithm, For time-frequency analysis algorithm, Sampling a priori probability, calculate the weight, Virtual power wireless sensor network coverage, Using MATLAB compressed sensing, Signal dimension estimates. Platform: |
Size: 7168 |
Author:aqrjftet |
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