pinghualvbo

filtro adaptivo funcion par q muestre la curvas

一个微软专家讲解的在mobile下开发时的一个测试视频(An expert on Microsoft in the mobile under development at the time of a test video)

remote sensing for texture

说明：  mstlab学习很好的资料，帮助你很快掌握该软件(mstlab learning good information to help you quickly master the software)

说明：  MATLAB是由美国mathworks公司发布的主要面对科学计算、可视化以及交互式程序设计的高科技计算环境。它将数值分析、矩阵计算、科学数据可视化以及非线性动态系统的建模和仿真等诸多强大功能集成在一个易于使用的视窗环境中，为科学研究、工程设计以及必须进行有效数值计算的众多科学领域提供了一种全面的解决方案，并在很大程度上摆脱了传统非交互式程序设计语言（如C、Fortran）的编辑模式，代表了当今国际科学计算软件的先进水平。　(MATLAB is a U.S. company' s main face mathworks scientific computing, visualization and high-tech interactive computing environment programming. It numerical analysis, matrix computation, scientific data visualization and nonlinear dynamic systems modeling and simulation, and many other powerful features in an easy to use integrated Windows environment for scientific research, engineering design and the need for effective numerical calculation many fields of science provides a comprehensive solution, and largely out of the traditional non-interactive programming language (such as C, Fortran) of the edit mode, on behalf of the current international advanced level of scientific computing software.)

这是一个简单的卡尔曼滤波的matlab仿真程序(This is a simple Kalman filter matlab simulation program)

电力电子、电机控制系统仿真模型 经典仿真源程序 洪乃刚版本(Power electronics, motor control system simulation model version of the classic simulation source Hong Naigang)

Zyuan光伏逆变仿真全套仿真例程，本文的主要目的就是通过z源升压之后通过mppt算法算出占空比控制控制逆变器实现光伏并网，(Zyuan photovoltaic inverter simulation full simulation routines, the main purpose of this paper is through the z source after booster by MPPT algorithm to calculate duty ratio control control realization of photovoltaic (pv) grid inverter,)

采用离子全算法的机组组合程序，用于求解电力系统的中含风电机组组合的问题(Particle Swarm Optimization for Unit program)

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.)