Modelling_systems_linear_objects_some_systems

DateConvert：简单而快速的日期转换 DateConvert转换DATESTR（0）格式的日期向量或序列号和日期向后。 Matlab的DATENUM，DATEVEC和DATESTR接受各种不同意见，但结果他们是缓慢的。如果输入格式正是众所周知，转换可以更快： DATENUM（ 17 - 10 - 2009 11时08分23秒 ）：2.97秒 DateConvert（ 17 - 10 - 2009 11时08分23秒 ， 号码 ）：0.039秒 =>“1.3％（1000环，1.5GHz的奔腾- M的，利用Matlab 7.7） DateConvert不*不*验证作为Matlab的日期函数输入日期： DATEVEC（ 32 - 2 - 2008 25:62:63 ）的答复[2008 3 4 2 2 7] DateConvert（ 32 - 2 - 2008 25:61:67 ， 载体 ）的答复[2008 2 32 25 61 67]！ 因此DateConvert几乎微不足道，但对速度进行了优化。我需要时间排序200 000邮票，并从217个减少到11秒的时间。 请运行单元测试TestDateVec检查的有效性和速度。 测试：利用Matlab 6.5，7.7，7.8，winxp的，32位。 纯Matlab代码，所以兼容性的MacOS，Linux操作系统，64位预期。 英语月份名称是必要的。(DateConvert: Simple but fast date conversion DateConvert converts the DATESTR(0) format to a date vector or serial date number and backwards. Matlab s DATENUM, DATEVEC and DATESTR accept a variety of inputs, but in consequence they are slow. If the input format is exactly known, the conversion can be much faster: DATENUM( 17-Oct-2009 11:08:23 ): 2.97 sec DateConvert( 17-Oct-2009 11:08:23 , number ): 0.039 sec => 1.3 (1000 loops, 1.5GHz Pentium-M, Matlab 7.7) DateConvert does*not* validate the input date as Matlab s date functions: DATEVEC( 32-Feb-2008 25:62:63 ) replies [2008 3 4 2 2 7], DateConvert( 32-Feb-2008 25:61:67 , vector ) replies [2008 2 32 25 61 67] ! So DateConvert is nearly trivial, but optimized for speed. I needed it for sorting 200 000 time stamps and reduced the time from 217 to 11 sec. Please run the unit-test TestDateVec to check validity and speed. Tested: Matlab 6.5, 7.7, 7.8, WinXP, 32bit. Pure Matlab code, so compat)

speech recognition, br recognition

By using this Matlab function The area and the x-y representation of a hysteresis loop can be estimated the image of the loop.

车辆在跟车过程中遭遇前车大的速度振荡时，后车的表现(Vehicle in the car with the car in the process of high speed before the oscillation, after the performance of the car)

to subtract background

高斯消元法程序，高斯消元法程序11111111(高斯消元法程序)

一个全新的GUI界面设置。。。希望大家喜欢！(A new GUI interface settings. . . I hope everyone likes!)

说明：  支持向量机最初用来解决模式识别问题，目的是发现泛化性能好的决策规则。然而，随着Vapnik的 不敏感损失函数的引入，支持向量机已经扩展为解决非线性回归估计问题，而且与神经网络方法相比，有着显著的优越性，被认为是人工神经网络方法的替代方法，已经成为目前机器学习领域的研究热点和焦点。(Support vector machine (SVM) was originally used to solve the problem of pattern recognition. However, with the introduction of Vapnik's insensitive loss function, support vector machine (SVM) has been extended to solve the problem of nonlinear regression estimation. Compared with neural network method, support vector machine (SVM) has obvious advantages. It is considered as an alternative method of artificial neural network method, and has become the research hotspot and focus in the field of machine learning.)

the file is the algorithm used for the color based seganography with multiple compression algorithms

一个直接转矩控制的仿真程序。可以用来了解直接转矩控制。(A direct torque control of the simulation program. Can be used to understand the direct torque control.)