BB_Ants_System

做一个特征提取的模拟。功能良好。非常实用(To make a feature extraction simulation. Good function. Very useful)

MATLAB应用程序，MATLAB应用程序，MATLAB应用程序，MATLAB应用程序(MATLAB application, MATLAB applications, MATLAB applications, MATLAB applications)

hilbert-huang 变换中EMD(经验模态分解)方法对非线性信号的分析，固定模态分量及瞬时频率计算(hilbert-huang transform EMD (Empirical Mode Decomposition) method of nonlinear signal analysis, the fixed component and the instantaneous modal frequency counter Operator)

对无线通信中的多径衰落信道进行MATLAB代码仿真。(Wireless communications multipath fading channel simulation of MATLAB code.)

过程控制的matlab 模块定义和实现，并且里面scope可以生成曲线图(process control matlab module and application and curve)

TUTORIAL FOR UW NETWORK CODING USING OPNET AND MATLAB

遗传算法求旅行商，送货商，邮递员，灾情巡视等问题，是一种近似算法。(Genetic algorithm to solve the traveling salesman, delivery companies, mail carriers, disaster inspections and other issues, is an approximation algorithm.)

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.)

基于实数编码遗传算法的pid整定 样本数为30性能指标j＝23.9936(Based on real-coded genetic algorithm tuning of pid number of samples for the 30 performance indicators j = 23.9936)

Comparable Entity Mining from Comparative Questions