DTFT

详细介绍遗传算法的MATLAB程序 完全可以运行 可用于对遗传的算法的理解我相关的跟踪中(Details on the MATLAB genetic algorithm procedure can be used to run the algorithm of genetic understanding, I related to tracking)

图像建模Markov随机场，希望对大家有帮助，呵呵。(images Markov modeling with the airport, and I hope to be helpful, huh.)

使用马尔科夫链蒙特卡洛法的一个莫诺模型例子(A Monod model example of the use of Markov chain Monte Carlo method)

设计信号用快速傅里叶变换求信号频谱功率谱(Fft spectrum signal design requirements)

svm code in matlab with smo method for optimization

GA-PLS遗传偏最小二乘法 用于数据挖掘或者是光谱特征变量筛选(GA-PLS Genetic Partial Least Squares)

说明：  高斯过程(GP)模型中推理和预测的实现。它实现了在《Rasmussen & Williams:机器学习的高斯过程》(麻省理工学院出版社，2006)和《Nickisch & Rasmussen:二进制高斯过程分类的近似》(JMLR, 2008)中讨论的算法。该函数的优点在于灵活性、简单性和可扩展性。该函数具有一定的灵活性，首先通过定义均值函数和协方差函数来确定遗传算法的性质。其次，它允许指定不同的推理过程，如精确推理和期望传播(EP)。第三，它允许指定似然函数，如高斯函数或拉普拉斯函数(用于回归)和累积逻辑函数(用于分类)。简单性是通过一个简单的函数和紧凑的代码实现的。可扩展性是通过模块化设计来保证的，允许为已经相当广泛的推理方法、均值函数、协方差函数和似然函数库轻松添加扩展。(Gaussian Processes for Machine Learning , the MIT press, 2006 and Nickisch & Rasmussen: Approximations for Binary Gaussian Process Classification , JMLR, 2008. The strength of the function lies in its flexibility, simplicity and extensibility. The function is flexible as firstly it allows specification of the properties of the GP through definition of mean function and covariance functions. Secondly, it allows specification of different inference procedures, such as e.g. exact inference and Expectation Propagation (EP). Thirdly it allows specification of likelihood functions e.g. Gaussian or Laplace (for regression) and e.g. cumulative Logistic (for classification). Simplicity is achieved through a single function and compact code.)

四杆机构运动求解，可运算关节角度，角加速度等(four-bar mechanism)

信道编码和无线电技术的一些MATLAB源代码(MATLAB source code of the channel coding and radio technology)

相关的 BSS 盲源分离问题和 CS压缩感知的结合4篇论文打包，以及一个声音信号混合分离的简单实现(the realization of BSS(blind source separation) and CS(compressed sensing), and a sample of volume signal s matlab illustration)