recognition-matlab-moving-objects

卡尔曼滤波器、粒子滤波器、贝叶斯滤波器的资料及实现，很全的，对于基础学习和提高都有帮助。希望对大家有帮助。(Kalman filter, particle filters, Bayesian filters the information and the realization of very wide, and for basic learning and helpful. Hope to help.)

一个神经网络的工具箱，用于M a t l a b中(A neural network toolbox for M atlab in)

用matlab仿真schmidl算法再ofdm系统的符号同步算法(Matlab simulation schmidl algorithm and thendm system symbol synchronization algorithm)

modified newton raphson的源程序，可以实现非线性elastic求解F或是displacement(modified newton raphsom)

用matlab实现了对于离散系列在时域上求其现行卷积(Achieved with matlab for the discrete series in the time domain convolution seek their existing)

线性与非线性光传输仿真。针对著名教材“Govind. P. Agrawal s text, "Nonlinear Fiber Optics”里的很多理论分析进行matlab仿真。(to simulate linear and nonlinear propagation in fiber. The examples reproduce many of the numerical results that are presented in Govind. P. Agrawal s text, "Nonlinear Fiber Optics)

This MATLAB M-file implements a finite-difference time-domain solution of Maxwell s curl equations over a one-dimensional space lattice comprised of uniform grid cells. To illustrate the algorithm, a 1-GHz sinusoidal wave propagating in a nonpermeable lossy medium (epsr=1.0, sigma=5.0e-3 S/m) is modeled. The simplified finite difference system for nonpermeable.

维进行维特比译码，数据为一千。特比译码（2，1，1）仿真,通过高斯白噪声,(Victoria Viterbi decoding, data for one thousand. Viterbi decoding (2,1,1) simulation by Gaussian white noise,)

设计自适应滤波器，实现对某一具体信号（如语音、图像、视频等）的处理。(Adaptive filter design, implement the processing of a specific signal (such as voice, images, video, etc.).)

《系统辨识与MATLAB仿真.pdf》 侯媛彬 汪梅 王立琦 本书共分8章，第1、2章为辨识的基本概念、理论基础和古典辨识方法；第3至6章为现代辨识内容，其中第3章是最小二乘参数辨识，第4章是梯度校正参数辨识，第5章是极大似然法的参数辨识方法，第6章是自适应参数辨识；第7、8章为复杂的非线性系统的智能辨识和混沌辨识，其中第7章是非线性系统的神经网络辨识；第8章是Volterra辨识方法、复杂系统的混沌现象及其辨识。从第2至7章，各章均包含开发的相应程序及其程序剖析。 (" System Identification and MATLAB simulation. Pdf" Hou Yuanbin Wang Mei Wang Liqi book is divided into eight chapters, Chapters 1 and 2 for the identification of the basic concepts, theoretical foundation and classical identification methods first 3-6 chapters of modern identification content, Chapter 3 is the least squares parameter identification, Chapter 4 is the gradient correction parameter identification, Chapter 5 is the maximum likelihood parameter identification method, Chapter 6 is an adaptive parameter identification Chapters 7 and 8 for complex Intelligent identification of nonlinear systems and chaos identification, which is in Chapter 7 of the neural network nonlinear system identification Chapter 8 is Volterra identification method, complex systems and chaos identification. From the first 2-7 chapters, each chapter contains procedures for the development of appropriate programs and their analysis.)