Comparetime-frequencydistribution

说明：  采用身高和体重数据作为特征，分别假设二者相关或不相关，在正态分布假设下估计概率密度，建立最小错误率Bayes分类器，写出得到的决策规则，将该分类器应用到训练/测试样本(Bayes classifier is the most classic! Minimize the classification error. With the matlab language, with examples)

In this paper, we show how support vector machine (SVM) can be employed as a powerful tool for $k$-nearest neighbor (kNN) classifier. A novel multi-class dimensionality reduction approach, Discriminant Analysis via Support Vectors (SVDA), is introduced by using the SVM. The kernel mapping idea is used to derive the non-linear version, Kernel Discriminant via Support Vectors (SVKD). In SVDA, only support vectors are involved to obtain the transformation matrix. Thus, the computational complexity can be greatly reduced for kernel based feature extraction. Experiments carried out on several standard databases show a clear improvement on LDA-based recognition

实现k维度的核密度估计，包含了点积核和常规核函数的实现(implement k-dimensional kernel density estimation， supports product kernels ，Gausiankernel 。)

有关系统辨识的课件以及相关辨识方法的matlab程序，很实用的。(The system identification of courseware and related identification methods matlab program, very useful.)

窗函数法设计数字滤波器——海明窗 非原创 原文章名称：基于窗函数法的FIR数字滤波器的设计 作者：张志田 何其文 湖南工业职业技术学院 电气工程系 刊载于湖南科技学院学报 2007年9月 第28卷9期(Window function method to design digital filters- Hamming window of non-original name of the original article: window function based on the design of FIR digital filters: Zhang Zhitian He Qiwen Hunan Vocational College of Electrical Engineering of Hunan University of Science published in 2007 Volume 28 on September 9)

基于累计量(SHIBBS)的实信号盲分离算法(Based on the cumulative amount of (SHIBBS) the real signal separation algorithm)

非线性加速度法计算单自由度体系地震时程反应,刚度矩阵，地震反应(Linear acceleration method to calculate the single degree of freedom system earthquake history response, stiffness matrix, seismic response)

用matlab做三角单元的有限元分析，很有用，代替手算，十分方便快捷。(Finite element analysis of triangular element with MATLAB )

仿真模拟了OFDM系统及QPSK 16QAM在高斯 瑞利和多径信道下的表现，并附有在独立衰落信道下的均衡器性能分析。(Simulation and QPSK 16QAM OFDM systems performance at Gaussian，Rayleigh and multi-path channel, along with independent channel fading equalizer performance analysis under.)

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)