technique-bootstrap-matlab

关于边缘检测各种算法的比较研究，包括roberts，sobel，prewitt，LOG，canny等五种(Edge detection algorithms on the comparative studies, including roberts, sobel, prewitt, LOG, canny and other five)

一本基于matlab的控制系统设计书籍，很详尽的介绍了如何应用matlab/simulink进行控制系统设计(A control system based on matlab books, describes in great detail how to use matlab/simulink for control system design)

The whole decoding process has been implemented natively on MATLAB. I need help in streamlining it so that it can run real-time. top level module is decoder.m. It takes in as input parameter the file name or file path and returns the decoded data & sampling frequency.

卡尔曼的视频序列帧图像的跟踪程序，基于matlab语言的，大家可以看看！(Tracking procedure of the Kalman frame of video sequences)

标准差分进化算法的MATLAB实现，有一个简单的函数，可以直接运行(MATLAB standard differential evolution algorithm to achieve, there is a simple function, can be run directly)

脑电中的递归式的处理方法，相比参数估计的方法更加智能，ＬＭＳ是最小均方误差法，是十分经典的一种方法，里面是包括一些最新的改进。(EEG recursive style approach, parameter estimation method compared to more intelligent, LMS is the minimum mean square error method is a very classical approach, which is to include some of the latest improvements.)

NSC program calculates BER of NSC

本程序是在Matlab6.5下编写的一个计算器的程序，是Matlab的GUI编程的很好例子。 (the procedure was Matlab6.5 prepared a calculator procedures, Matlab GUI Programming good example.)

讨论匹配滤波器对不同形式的PAM信号进行接收的情况。并研究在高斯白噪声环境中传输的改善程度(Discussion of the matched filter for different forms of PAM signal reception conditions. And to study the white Gaussian noise environment in the improvement of transmission)

多变量输入、输出、多干扰、非线性和强耦合的复杂系统控制是一个比较困难的问题，常用的控制器可能因为多变量耦合问题难以控制系统。PID神经元网络是一种多层前向神经元网络，它的各层神经元个数、连接方式、连接权值是按照PID控制规律的已有原则和经验确定的，是一种动态的符合控制系统的前向网络。但是由于PID网络初始权值随机取值的原因，每次控制的效果都有所差别，个别情况下控制效果还比较差。本案例研究了基于PID神经元的多变量耦合系统控制，并用PSO算法来优化控制器以取得更好的控制效果。(Multivariable input, output, and more interference, complex nonlinear and strong coupling system control is a more difficult problem, commonly used multivariable controller may be because the problem is difficult to control the coupling system. PID neural network is a multi-front to neural networks, its number of neurons in each layer, connection, connection weights are in accordance with the existing principles and experience to determine the PID control law is a dynamic compliance before the control system to the network. However, due to the random initial weights of the network PID values ​ ​ of reason, every time there are differences in the effect of control and in some cases the control effect is still relatively poor. This case study of multivariable coupling system based on PID control neurons, and with PSO algorithm to optimize the controller to achieve better control effect.)