multi-satellite-GPSsignal

matlab中文课件，ppt格式Matlab基础及其应用(matlab Chinese courseware, ppt format based on Matlab and Its Application)

张小飞(南京航空航天大学） 阵列自适应信号处理的部分代码，包括 MUSIC算法，ESPRIT算法 ROOT-MUSIC算法 等(Xiaofei (Nanjing University of Aeronautics) adaptive array signal processing part of the code, including the MUSIC algorithm, ESPRIT algorithm ROOT-MUSIC algorithm)

说明：  基于matlab的电力系统潮流计算 实现潮流计算的快捷 并进行数据的验算(The power flow computation of power system based on MATLAB)

模糊控制作业，采用模糊控制方法实现，闭环控制，并与传统PID控制比较说明模糊控制的优越性(Operations of fuzzy control, fuzzy control method to achieve closed-loop control, and comparison shows the superiority of fuzzy control and conventional PID control)

偶编写的倒立摆系统的MATlab关于模糊自适应控制的对象和控制器S函数，以及Simulink模块文件，一个完全实现的倒立摆程序，发表文章很有用(Even the preparation of the inverted pendulum system on the Matlab fuzzy adaptive control of the object and the controller S function, as well as the Simulink module files, a full realization of the inverted pendulum process, published an article very useful)

基于最小二乘支持向量机对传感器的线性系统用MATLAB软件进行仿真建模程序(Based on least square support vector machine (SVM) of the linear system of sensor with MATLAB simulation modeling software program )

在脉冲噪声情况下，基于分数低阶统计量的循环平稳信号的波达方向估计(In the case of impulse noise, DOA fractional lower order statistics of cyclostationary signal estimate)

MATLAB给图像添加高斯、椒盐、加性及乘性噪声[噪声生成]源代码Gaussian-Pepper-Noise-Generator(MATLAB——Gaussian-Pepper-Noise-Generator)

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感知器感知器算法训练二元线性分类器。结构体数据使用感知器学习规则 找到给定的二分类标签数据超平面。 (PERCEPTRON Perceptron algorithm to train binary linear classifier. Synopsis: model = perceptron(data) model = perceptron(data,options) model = perceptron(data,options,init_model) Description: model = perceptron(data) uses the Perceptron learning rule to find separating hyperplane from given binary labeled data. model = perceptron(data,options) specifies stopping condition of the algorithm in structure options: .tmax [1x1]... maximal number of iterations. If tmax==-1 then it only returns index (model.last_update) of data vector which should be used by the algorithm for updating the linear rule in the next iteration. model = perceptron(data,options,init_model) specifies initial model which must contain: .W [dim x 1] ... normal vector. .b [1x1] ... bias of hyperplane. .t [1x1] (optional) ... iteration number. Input: data [struct] Labeled (binary) training data. .X [dim x num)