Description: 基于神经网络的PID控制不是用神经网络来整定PID的参数，而是用神经网络直接作为控制器，通过训练神经网络的权系数间接地调整PID参数。-based on neural network PID control is not using neural networks to PID parameters, Rather, as a neural network controller directly, through the training of the neural network weights indirectly PID parameters to adjust. Platform: |
Size: 69632 |
Author:戚伟 |
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Description: 该程序专门用于检验bp神经网络的连接权值-devoted to the testing procedures bp neural networks and the connection weights Platform: |
Size: 1024 |
Author:张凌 |
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Description: nnToolKit 神经网络工具包是基于 MATLAB 神经网络工具箱自行开发的一组神经网络算法函数库，其中的函数在MATLAB环境下均可独立运行，并可打包成DLL组件，这些组件作为独立的COM对象，可以直接被Visual Basic、Visual C++ 、C++ Builder或其它支持COM的高级语言所引用。本工具包中包含的算法包括BP算法中的一些改进算法、模糊神经网络、小波神经网络、遗传算法优化神经网络权值改进算法等，根据需要，读者还可以对该工具包进行扩展，即扩充一些新的函数，以实现特殊的算法需求。-nnToolKit neural network tool kit is based on the MATLAB neural network toolbox developed by a group of neural network algorithm library, which functions in the MATLAB environment can be run independently, and packaged into a DLL components, these components as a separate COM object, can be directly by Visual Basic, Visual C++, C++ Builder or other high-level language support COM quoted. The toolkit includes algorithms, including some of the BP algorithm to improve the algorithm, fuzzy neural network, wavelet neural networks, genetic algorithms to optimize neural network weights to improve the algorithm, if necessary, readers can also expand the tool kit, namely the expansion of some new function, the algorithm in order to achieve special needs. Platform: |
Size: 10865664 |
Author:魏建明 |
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Description: 基于bp神经网络的车牌字符识别源程序，采用字符比对的方式进行切割-The structures of the neural networks were designed using a constructive algorithm where the basic idea was to start with a small network,then add hidden units and weights incrementally until a satisfactory solution be found .The higher efficiencies were for a 30×17×10 number network and letter network Platform: |
Size: 488448 |
Author:maliguo |
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Description: 用遗传算法优化bp神经网络的权值，并训练以及验证网络-Bp by using genetic algorithms to optimize the weights of neural networks, and network training and validation Platform: |
Size: 2048 |
Author:ruoshui |
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Description: 为了选择神经网络的最好结构以及增强模型的推广能力，提出一种自适应支持向量回归神经网络(SVR—NN)。SVR—NN 用支持向量回归(SVR)方法获得网络的初始结构和权值， 白适应地生
成网络隐层结点，然后用基于退火过程的鲁棒学习算法更新网络结点疹教和权 主。 SVR—NN有很
好的收敛性和鲁棒性，能抑制由于数据异常和参数选择不当所导致的“过拟合，’现象。将SVR—NN
应用到时间序列预测上。结果表明，SVR．NN预测模型能精确地预测混沌时间序列，具有很好的
理论和应用价值。-Abstract：To select the‘best’structure of the neural networks and enhance the generalization ability of models．a support
vector regression neural networks fSVR-NN)was proposed．Firstly，support vector regression approach was applied to
determine initial structure and initial weights of SVR．NN SO that the number of hidden layer nodes can be constructed
adaptively based on support vectors．Furthermore，an annealing robust learning algorithm was further presented to fine
tune the hidden node parameters and weights of SVR一ⅣM The adaptive SVR．NN has faSt convergence speed and robust
capability．and it can also suppress the ‘orerfitting’phenomena when the train data ncludes outliers．The adaptive
SVR．NN was then applied to time series prediction．Experimental results show that the adaptive SVR．ⅣⅣ can accurately
predict chaotic time series，and it iS valuable in both theory and application aspects． Platform: |
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Author:11 |
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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: 随机神经网络(en: Stochastic Neural Networks)是一种人工神经网络，并作为人工智能的工具。它们向神经网络引进随机变化，一类是在神经元之间分配随机过程传递函数，一类是给神经元随机权重。-Random Neural Network (en: Stochastic Neural Networks) is an artificial neural network and artificial intelligence as a tool. They introduce random changes to the neural network, one is assigned a random process transfer function between neurons, one is to neurons random weights. Platform: |
Size: 3072 |
Author:qjl |
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Description: 包括最小二乘法、SVM、神经网络、1_k近邻法,ncdAMAI参数实现了对10个数字音的识别程序相控阵天线的方向图（切比雪夫加权），现代信号处理中谱估计在matlab中的使用，nvbOUML条件基于互功率谱的时延估计，实现了对10个数字音的识别。- Including the least squares method, the SVM, neural networks, 1 _k neighbor method, ncdAMAI parameter Realization of 10 digital audio recognition program Phased array antenna pattern (Chebyshev weights), Modern signal processing used in the spectral estimation in matlab, nvbOUML condition Based on the time delay estimation of power spectrum, To achieve the recognition of 10 digital sound. Platform: |
Size: 5120 |
Author:msnftd |
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Description: 在matlab R2009b调试通过，与理论分析结果相比，相控阵天线的方向图（切比雪夫加权），实现串口的数据采集，包括最小二乘法、SVM、神经网络、1_k近邻法,在matlab环境中自动识别连通区域的大小,采用波束成形技术的BER计算。- In matlab R2009b debugging through, Compared with the results of theoretical analysis, Phased array antenna pattern (Chebyshev weights), Achieve serial data acquisition, Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Automatic identification in the matlab environment the size of the connected area, By applying the beam forming technology of BE. Platform: |
Size: 6144 |
Author:baysfwc |
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