NewtonRaphson_0.5

说明：  多目标优化，遗传算法，适合于研究多目标优化的朋友 (Multi-objective optimization, genetic algorithm, suited to study multi-objective optimization of a friend)

gstreamer 学习资料汇总 包括开发架构 程序用例 以及具体实现(gstreamer learning materials development framework program summary includes use cases and implementation)

说明：  波束调向直线阵 边射阵和端射阵是一般调向直线阵的特例(Beam transfer line array broadside to the array and end-fire array is generally transferred to the special case of linear array)

利用电流源模拟光伏输出，同时实现单相并网的matlab仿真，在其中模拟实现了光伏的mppt控制，同时实现了光伏电池的最大能量并网。(Use current source simulation of PV output, single-phase grid matlab simulation, in which the analog implementation of PV mppt control, while achieving maximum energy photovoltaic cells and networks.)

语音信号的lpc分析，该程序实现了与语音预测器的阶数与预测残差能量的关系(The speech signal LPC analysis, the program with the order of the prediction residual energy of the speech predictor)

说明：  SURROGATES工具箱是一个多维函数逼近和优化方法的通用MATLAB库。当前版本包括以下功能： 实验设计：中心复合设计，全因子设计，拉丁超立方体设计，D-optimal和maxmin设计。 代理：克里金法，多项式响应面，径向基神经网络和支持向量回归。 错误和交叉验证的分析：留一法和k折交叉验证，以及经典的错误分析（确定系数，标准误差;均方根误差等;）。 基于代理的优化：高效的全局优化（EGO）算法。 其他能力：通过安全裕度进行全局敏感性分析和保守替代。(SURROGATES Toolbox is a general-purpose MATLAB library of multidimensional function approximation and optimization methods. The current version includes the following capabilities: Design of experiments: central composite design, full factorial design, Latin hypercube design, D-optimal and maxmin designs. Surrogates: kriging, polynomial response surface, radial basis neural network, and support vector regression. Analysis of error and cross validation: leave-one-out and k-fold cross-validation, and classical error analysis (coefficient of determination, standard error; root mean square error; and others). Surrogate-based optimization: efficient global optimization (EGO) algorithm. Other capabilities: global sensitivity analysis and conservative surrogates via safety margin.)

ieee papers on image compression

modular fuel cell model

数据矩阵运用非负矩阵分解算法，分解得到两个低维矩阵，并用于信号分离。(The data matrix is decomposed into two low-dimensional matrices by non-negative matrix decomposition algorithm and used for signal separation.)

均匀量化和非均匀量化在性能上存在着显著的差异，这种差异可以用信号通过量化器后的量躁比来反映出来。本程序用曲线表示了理论和实际两个方面下两种量化的量躁比。从而可观察他们的性能上的差别。(There are significant differences with the function between Uniform quantization and non-uniform quantization ,which can be quantified by SNR from the quantizer.This program use the curves to denote the SNR of theory and practical signal.Then we can observe the performance of the differences.)