fuzzy_system_data

带遗忘因子的线性回归算法程序，适用于时变特性的数据(Data with forgetting factor linear regression algorithm procedures applicable to the time-varying characteristics)

说明：  对采集到的数据进行分析，并有相对的频谱转换，观察频域的信号(On analysis of data collected, and the relative frequency conversion, frequency domain signal observed)

matlab 入门 matlab 基本操作方法及入门介绍(matlab introduction)

学习matlab的好资料,是你学习的好助手，你可以用来学习matlab和数学建模(this is a learn matlab very good )

DWT of image using haar wavelet

常见的buffer设计，包括：Fast ring buffer, Loop buffer, Bip buffer， circular character buffer，可以方便的集成到相关代码中(common buffer example and code, including fast ring buffer, loop buffer, bip buffer, circular character buffer, and they can integrate to relative code easily.)

基于脉冲反射法定位电缆故障的M128代码,组合采样法对固定波形数据采样提高采样率。(Based on the pulse reflection method to locate cable faults M128 code, fixed combination waveform sampling data sampling to increase the sampling rate.)

小波光谱去噪源代码，包含三种阈值去噪方法和误差分析(Wavelet Spectra Denoising source code, including the three threshold denoising method and error analysis)

说明：  MIT Matlab教程：INTRODUCTION TO MATLAB — Fall 1999 Thomas F. Weiss Last modification September 9, 1999 1(MATLAB is a programming language and data visualization software package which is especially effective in signal processing and systems analysis. This document is a brief introduction to MATLAB that focuses on those features that are of particular importance in 6.003.1 It is assumed that the reader is familiar with Project Athena, has an Athena account, and has little or no experience with MATLAB. Other MATLAB help is available through Athena consulting which offers a number of more tutorial handouts and short courses (ext. 3-4435), on-line consulting (type olc at the Athena prompt), and Athena on-line help (type help at the Athena prompt). There are a number of books available that describe MATLAB. For example, Engineering Problem Solving with Matlab, by D. M. Etter, published by Prentice-Hall (1997) and Mastering MATLAB, by Hanselman and Littlefield, published by Prentice-Hall (1996). The paperback MATLAB Primer by K. Sigmon, published by CRC Press (1994) is a handy summar)

[model] = SVMTRAIN(X, Y, C, kernelFunction, tol, max_passes) trains an SVM classifier and returns trained model. X is the matrix of training examples. Each row is a training example, and the jth column holds the jth feature. Y is a column matrix containing 1 for positive examples and 0 for negative examples. C is the standard SVM regularization parameter. tol is a tolerance value used for determining equality of floating point numbers. max_passes controls the number of iterations over the dataset (without changes to alpha) before the algorithm quits.