matlab

fuzzy tool box的详细用户手册指南(fuzzy tool box of the detailed user manual guide)

自适应模糊控制的MATLAB/simulink仿真。(MATLAB/simulink simulation of adaptive fuzzy controller.)

VC调用matlab混合编程 利用matlab引擎(VC call matlab hybrid programming using matlab engine)

matlab程序，关于BP神经网络的，修正函数，变学习速率(matlab program on BP neural network, correction function, variable learning rate)

This is the code of speech processing.

说明：  有序统计均值类恒虚警检测器（OSCAGO and OSCASO CFAR），综合了CA和OS检测器的特点。(detectors based on ordered statistic and cell averaging constant false alarm rate (OSCA-CFAR))

MATLAB编程实例100例。。。。。。。。。。(100 cases of examples of MATLAB programming. . . . . . . . . .)

matlab 智能算法30例介绍，其中有背景，分析，程序，供大家参考。(matlab introduce intelligent algorithms 30 cases, including the background, analysis, procedures, for your reference.)

遗传算法自编程序，自己编写的matlab程序，没有用到工具箱(Genetic algorithms written procedures,)

GloptiPoly 3: moments, optimization and semidefinite programming. Gloptipoly 3 is intended to solve, or at least approximate, the Generalized Problem of Moments (GPM), an infinite-dimensional optimization problem which can be viewed as an extension of the classical problem of moments [8]. From a theoretical viewpoint, the GPM has developments and impact in various areas of mathematics such as algebra, Fourier analysis, functional analysis, operator theory, probability and statistics, to cite a few. In addition, and despite a rather simple and short formulation, the GPM has a large number of important applications in various fields such as optimization, probability, finance, control, signal processing, chemistry, cristallography, tomography, etc. For an account of various methodologies as well as some of potential applications, the interested reader is referred to [1, 2] and the nice collection of papers [5].(GloptiPoly 3: moments, optimization andsemidefinite programming.Gloptipoly 3 is intended to solve, or at least approximate, the Generalized Problem ofMoments (GPM), an infinite-dimensional optimization problem which can be viewed asan extension of the classical problem of moments [8] . From a theoretical viewpoint, theGPM has developments and impact in various areas of mathematics such as algebra, Fourier analysis, functional analysis, operator theory, probability and statistics, to citea few. In addition, and despite a rather simple and short formulation, the GPM has alarge number of important applications in various fields such as optimization, probability, finance, control, signal processing, chemistry, cristallography, tomography, etc. For anaccount of various methodologies as well as some of potential applications, the interestedreader is referred to [ 1, 2] and the nice collection of papers [5].)