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matlab中的Kriging 算法，可以有Kriging的多种算法(The Kriging algorithm in matlab)

ook程序 关于课程设计的编程设计 通信专业 (the ook program Programming communications professionals on curriculum design)

DUCOMENT ABOUT AMPLIFIER OPTICAL

静止无功补偿器 直接电流控制法的matlab模型(The matlab model of direct current control method for static reactive compensator.)

自相关虽然能够判断清/浊音，并估计浊音的基音周期，但是其运算量很大，用乘法运算所需时间较长。为了避免乘法，采用短时平均幅度差函数（AMDF），验证AMDF与自相关有类似的作用，即平均幅度差函数能够代替自相关函数进行语音分析。(Although autocorrelation can differ from the Voiced/Unvoiced and voiced the pitch period estimate, but the operation is large enough, with the multiplication time was needed. In order to avoid the multiplication using the short time average magnitude difference function (AMDF), verify the AMDF and autocorrelation have a similar effect, that the average magnitude difference function can replace the voice of the autocorrelation function.)

简单的信号处理程序,可以实现基本的z反变换。程序本身自带数据产生功能。(a simple signal process program)

有CDF三角函数曲线/三维曲线图，欢迎大家下载学习，三相光伏逆变并网的仿真，LCMV优化设计阵列处理信号，分析了该信号的时域、频域、倒谱，循环谱等。( There CDF trigonometric curve/3D graphs, Welcome to download the study, Three-phase photovoltaic inverter and network simulation, LCMV optimization design array signal processing, Analysis of the signal time domain, frequency domain, cepstrum, cyclic spectrum, etc.)

姿态角辅助的机动目标跟踪算法，实现了异类信息的融合，提高了跟踪精度。(pose aided maneuvering target tracking algorithm, which fused diffeent kind of information and improved the tracking precision.)

snake为从网上下载的程序包,该程序包内容较全，涉及了snake的很多改进形式，可以参考。(A good program bag of snake)

说明：  最速下降法是迭代法的一种,可以用于求解最小二乘问题(线性和非线性都可以)。在求解机器学习算法的模型参数，即无约束优化问题时，梯度下降（Gradient Descent）是最常采用的方法之一，另一种常用的方法是最小二乘法。在求解损失函数的最小值时，可以通过梯度下降法来一步步的迭代求解，得到最小化的损失函数和模型参数值。反过来，如果我们需要求解损失函数的最大值，这时就需要用梯度上升法来迭代了。在机器学习中，基于基本的梯度下降法发展了两种梯度下降方法，分别为随机梯度下降法和批量梯度下降法。(The steepest descent method is a kind of iterative method, which can be used to solve the least squares problem (both linear and nonlinear). In solving the model parameters of machine learning algorithm, that is, unconstrained optimization, gradient descent is one of the most commonly used methods, and the other is the least square method. When solving the minimum value of loss function, the gradient descent method can be used step by step to get the minimum value of loss function and model parameters. Conversely, if we need to solve the maximum value of the loss function, then we need to use the gradient rise method to iterate. In machine learning, two kinds of ladders are developed based on the basic gradient descent method)