wave-nn

lab simulinl, it s my lad in shool

无约束多维极值问题,适合数学计算,优化分析等极值问题的求解(Unconstrained multi-dimensional extremum problem, is suitable for mathematical calculations, the extremum problems of solving optimization analysis, etc )

This thesis describes methods for solving certain optimization problems commonly arising in computer vision by PETTER STRANDMARK

雷达课程的作业，在了解雷达原理后，对实际过程的仿真，有Chirp的形成，匹配滤波器，MTI，MTD的具体实现(Concrete realization of radar courses operating in the understanding of the radar principle, the simulation of the actual process, the formation of the Chirp matched filter, MTI, MTD)

不平衡电网条件下，逆变器根据并网准则的要求，需要不脱网运行，并提供无功支撑。本仿真实现了不平衡条件下的逆变器低穿运行，并实现在故障后快速的功率回复(Under unbalanced grid conditions, the inverter according to the guidelines and requirements of the network, you need not run off the network, and provide reactive support. The Simulation of the inverter under unbalanced conditions, low wear operation, and after the failure to quickly realize the power of reply)

ROC curves illustrate performance on a binary classification problem where classification is based on simply thresholding a set of scores at varying levels. Lenient thresholds give high sensitivity but low specificity, strict thresholds give high specificity but low sensitivity the ROC curve plots this trade-off over a range of thresholds (usually with sens vs 1-spec, but I prefer sens vs spec this code gives you the option). It is theoretically possible to operate anywhere on the convex hull of an ROC curve, so this is plotted too. The area under the curve (AUC) for a ROC plot is a measure of overall accuracy, and the area under the ROCCH is a kind of upper bound on what might be achievable with a weighted combination of differently thresholded results from the given classifier

说明：  Matlab下美军F16战斗机的非线性模型(Nonlinear F-16 Fighter Model)

这里实现了基于四种SVM工具箱的分类与回归算法: 1、工具箱：LS_SVMlab Classification_LS_SVMlab.m - 多类分类 Regression_LS_SVMlab.m - 函数拟合 2、工具箱：OSU_SVM3.00 Classification_OSU_SVM.m - 多类分类 3、工具箱：stprtoolsvm Classification_stprtool.m - 多类分类 4、工具箱：SVM_SteveGunn Classification_SVM_SteveGunn.m - 二类分类 Regression_SVM_SteveGunn.m - 函数拟合 更详细的相关函数说明请通过help命令查看！(Here the realization of the four SVM toolbox based on the classification and regression algorithm: 1, Toolbox: LS_SVMlabClassification_LS_SVMlab.m- Multiclass Classification Regression_LS_SVMlab.m- function fitting 2, the toolbox: OSU_SVM3.00Classification_OSU_SVM.m- Multiclass Classification 3, Toolbox: stprtoolsvmClassification_stprtool.m- Multiclass Classification 4 toolbox: SVM_SteveGunnClassification_SVM_SteveGunn.m- II Category Regression_SVM_SteveGunn.m- function fitting a more detailed explanation of the correlation function through the help command to view!)

基于MATLAB的仿真，伪随机序列的应用和产生。对于学习和工程应用有帮助。(MATLAB-based simulation, the application of pseudo-random sequences and generation. For help learning and engineering applications.)

文章展示了基于高斯混合模型的语音频谱预测方法。频谱预测可能在传包过程中预防丢包这方面起到大作用。期望最大化算法用两倍或三倍的连续语音因素来测试模型。模型被用来设计第一，儿等指令预测量。预测表用频谱分配状态来估计并和一个简单的参考模型对比。最好的预测表得到一个平均频率扭曲值是0.46dB小于参考模型(This paper presents methods for speech spectrum prediction based on Gaussian mixture models. Spectrum prediction may be useful in a packet transmission system where the sensitivity to packet losses is a major problem. Models of speech are trained by the Expectation Maximization algorithm using pairs, triples etc. of consecutive cepstral vectors. The models are used to design first, second etc. order predictors. The prediction schemes are evaluated using the spectral distortion criterion and compared to a simple reference method. The best prediction scheme obtains an average spectral distortion that is 0.46 dB less than for the reference method. )