gls

提供了MATLAb针对光谱数据分析的PLS算法(MATLAB sepctronic ’s pls)

惯导轨迹仿真，对于初学惯导的人有很大的帮助(INS trajectory simulation, inertial navigation for beginners who have a lot of help)

Android Battery Dog手機電量監控介面原始碼(Cell phone battery monitoring interface)

This tutorial illustrates the basic ideas of interval type-2 (IT2) fuzzy sets and systems, and provides a Matlab implementation of IT2 fuzzy logic system (FLS). One obstacle in learning IT2 fuzzy logic is its complex notations. In this tutorial we try to avoid these notations and give the reader some intuitive understanding of IT2 FLSs. We also briefl y discuss approaches for reducing the computational cost of IT2 FLSs and the fundamental differences between IT2 and type-1 FLSs.

关于最优化算法的Matlab仿真程序，最速下降法与牛顿法。(Optimization algorithm Matlab simulation program, the steepest descent method and Newton method.)

qpsk的方波滤波，利用matlab编写M文件，调试成功，并描绘滤波图形。(the qpsk square wave filter using Matlab prepared M-file debugging success, and a description of the filter graphics.)

ICP version 2. Removes one to many correspondences.

基于X12的季节调整模型的matlab程序(X12 seasonal adjustment model based on matlab program)

该算法用于自回归输入模型，是一种迭代的算法。其基本思想是基于对数据先进行一次滤波处理，后利用普通最小二乘法对滤波后的数据进行辨识，进而获得无偏一致估计。但是当过程的输出信噪比比较大或模型参数较多时，这种数据白色化处理的可靠性就会下降，辨识结果往往会是有偏估计。数据要充分多，否则辨识精度下降。模型阶次不宜过高。初始值对辨识结果有较大影响。(The algorithm used for autoregressive input model, it is a kind of iterative algorithm. The basic idea is based on data to conduct a filtering processing, after using ordinary least square method to identify the data filter, and then obtain unbiased consensus estimates. But when the process output signal-to-noise ratio is larger or model parameters are too, this kind of data white processs reliability will drop, identification results tend to be biased estimate. Data should fully, otherwise the identification accuracy down. Model order time shoulds not be too high. Initial value to identification results have great influence on.)

加有高斯白噪声的两个正弦信号的的观测数据，分别用最小二乘法和总体最小二乘法估计其功率谱，并对两种方法的谱分析进行了比较(Plus a Gaussian white noise of the two sinusoidal signals of observational data, were used least squares and total least squares method to estimate the power spectrum, and spectral analysis of the two methods were compared)