hhosu

powell code for matlab

说明：  简单的预测与案例教程，适合初学者用于学习预测原理和简单的matlab代码(failed to translate)

神经网络进行预测matlab，很不错，值得看看(Neural network prediction matlab, very good, worth a look)

一个用来学习和使用的标准kalman和lms变步长最小均方滤波算法源程序代码(One source code to learn and use the standard kalman and least mean square filtering algorithm)

gaca源码，多用于mp4格式的文件。编译可产生一些重要的库。(gaca source, then no time for the multi-format document. Compiler can produce some major libraries.)

RuimSmsInterfaceManager to provide an inter-process communication to access Sms in Ruim.

说明：  EMD matlab实现代码，可以直接用。经验模态分解（Empirical Mode Decomposition，EMD）是由 Huang等人于1998年提出的一种针对非线性、非平稳信号的自适应信号分解算法。(Empirical Mode Decomposition (EMD) is an adaptive signal decomposition algorithm for non-linear and non-stationary signals proposed by Huang et al. in 1998.)

RSA Encryption and Decryption using Matlab by Thunyawat Rajatasereekul and Voranon Kiettrsalpipop ECE575, Winter 2002, Oregon State University Prof, Cetin Keya Koc The program set contains thirteen files listed below. errormeg.fig errormsg.m helpmsg.fig helpmsg.m inputmsg.fig inputmsg.m mesgcut.m pro2.fig pro2.m rsacore.m readme.txt screenshot.gif In order to run the program please call pro2.m under Matlab 6.0 Environment. System Requirement Matlab version 6.0 and if necessary, Maple version 6.0 on any platform PentiumII 300 MHz or equivalent.(RSA Encryption and Decryption using Matlabby Thunyawat Rajatasereekul and Voranon KiettrsalpipopECE575, Winter 2002, Oregon State UniversityProf, Cetin Keya KocThe program set contains thirteen files listed below.errormeg.figerrormsg.mhelpmsg.fighelpmsg.minputmsg.figinputmsg.mmesgcut.mpro2.figpro2.mrsacore. mreadme.txtscreenshot.gifIn order to run the program please call pro2.m under Matlab 6.0 Environment.System RequirementMatlab version 6.0 and if necessary, Maple version 6.0 on any platformPentiumII 300 MHz or equivalent.)

说明：  PN code generation for any length of generator polynomial

计算统计手册采用matlab实现，书中详细介绍了数理统计的各种方法给出了对应的matlab程序，包括时下比较热门的非参数估计（PDF估计等）(There are wonderful books that cover many of the techniques in computational statistics and, in the course of this book, references will be made to many of them. However, there are very few books that have endeavored to forgo the theoretical underpinnings to present the methods and techniques in a manner immediately usable to the practitioner. The approach we take in this book is to make computational statistics accessible to a wide range of users and to provide an understanding of statistics from a computational point of view via algorithms applied to real applications.)