lms-nlms

这是一个求盒维数的Matlab程序，具有简单易求的功能(This is a box-counting dimension for the Matlab program, with simple and easy for the function of)

This lab exercise will introduce you to AccelDSP’s floating- to fixed-point conversion features. AccelDSP will automatically generate a fixed-point representation of a floating-point design. This process is controllable by using quantize directives.(This lab exercise will introduce you to Acc elDSP's floating-to fixed-point conversion f eatures. AccelDSP will automatically generat e a fixed-point representation of a floating-p oint design. This process is controllable by us ing quantize directives.)

非线性光学中光纤材料色散随波长变化的曲线仿真 matlab(Nonlinear optical fiber material dispersion versus wavelength curve simulation matlab)

lms算法仿真，及其主要性能分析。步长的影响与学习曲线的分析。(lms algorithm simulation, and its main performance analysis. Step of the analysis and the learning curve.)

The simulink model of Back propagation Algorithm using an Adaptative Filter.

带通和带阻滤波器的设计，包含原始信号，编码，以后仿真之后的信号(a design for filter,and including original signal and the image after simulation.)

画图用的matlab程序希望大家能喜欢，不会的密我(Matlab drawing program with the hope that we can like, I will not close)

802.11 SIMULATION REPORT AND SOURCE CODE

This folder contains Matlab functions for the Enhanced Interval Approach (EIA) in: Simon Coupland, J. M. Mendel and Dongrui Wu, “Enhanced Interval Approach for Encoding Words into Interval Type-2 Fuzzy Sets and Convergence of the Word FOUs,” IEEE World Congress on Computational Intelligence, Barcelona, Spain, July 2010. All following functions were tested in Matlab 7.4.0 (R2007a) under Windows 7: centroidIT2: Compute the centroid of an IT2 FS. EKM: Implement the Enhanced KM algorithms. IA: Implement the Interval Approach for word encoding. mainJPL: Illustrate IA and EIA using the JPL dataset. mainWeb: Illustrate IA and EIA using the Web dataset. mg: Compute the membership grades of a vector of numbers on a T1 FS. plotFigures: Plot Figs. 3.18, 3.19 and 3.20 in the book. plotIT2: Plot an IT2 FS. Last modified by Dongrui Wu (dongruiw@usc.edu), May 2, 2010.

Regularized Linear Regrassion and Bias v.s. Variance