sjm

matlab的函数大全，挺有用的，不错哦！(function about matlab，very useful！)

MATLAB模糊神经网络的一些代码，算法。(MATLAB fuzzyNN)

永磁同步电机matlab，simulink 的svpwm仿真(Svpwm vector control of permanent magnet synchronous motor simulink simulation )

仿真（ZF,ZF-SIC,ZF-OSIC,MMSE,MMSE-SIC,MMSE-OSIC,ML)Vblast接收机的检测性能，绘制误比特率～信噪比曲线(Simulation (ZF, ZF-SIC, ZF-OSIC, MMSE, MMSE-SIC, MMSE-OSIC, ML) detection performance Vblast receiver bit error rate ~ SNR plotted curve)

三维数字滤波器中的高斯理想低通滤波器的matlab仿真及其结果(3 d digital filter of gaussian ideal low-pass filter matlab simulation and the result )

低通滤波程序，很好，很实用，用于信号处理过程中的滤波(very good )

3d evalation code download

模拟退火算法的基本思想是从一给定解开始,从邻域中随机产生另一个解,接受Metropolis准则允许目标函数在有限范围内变坏,它由一控制参数t决定,其作用类似于物理过程中的温度T,对于控制参数的每一取值,算法持续进行“产生—判断—接受或舍去”的迭代过程,对应着固体在某一恒定温度下的趋于热平衡的过程,当控制参数逐渐减小并趋于0时,系统越来越趋于平衡态,最后系统状态对应于优化问题的全局最优解,该过程也称为冷却过程,由于固体退火必须缓慢降温,才能使固体在每一温度下都达到热平衡,最终趋于平衡状态,因此控制参数t经缓慢衰减,才能确保模拟退火算法最终优化问题的整体最优解。(Simulated annealing algorithm based on the idea that a given solution from the beginning, randomly generated from the adjacent domain to another solution, Metropolis acceptance criteria for the objective function to allow a limited range of deterioration, which is a control parameter t decided that its role is similar to the physical the course of the temperature T, the control parameters for each value, the algorithm continued to "have- to determine- to accept or rounding" of the iterative process, corresponding to a solid at a constant temperature of the process tends to thermal equilibrium, when control parameters and tended to decrease gradually 0:00, the system equilibrium state more and more, and finally the system state corresponds to the optimization problem of global optimal solution, the process is also known as the cooling process must be slow as a result of annealing temperature of solid to make solid temperature in each of them had attained thermal equilibrium, and ultim)

说明：  盲源分离FastICA、matalab程序，(FAST KERNEL ICA | |--------------------| Version 1.0- February 2007 Copyright 2007 Stefanie Jegelka, Hao Shen, Arthur Gretton This package contains a Matlab implementation of the Fast Kernel ICA algorithm as described in [1]. Kernel ICA is based on minimizing a kernel measure of statistical independence, namely the Hilbert-Schmidt norm of the covariance operator in feature space (see [3]: this is called HSIC). Given an (n x m) matrix W of n samples from m mixed sources, the goal is to find a demixing matrix X such that the dependence between the estimated unmixed sources X *W is minimal. FastKICA uses an approximate Newton method to perfom this optimization. For more information on the algorithm, read [1], and for more information on HSIC, refer to [3]. The functions chol_gauss and amariD are taken from and based on, respectively, code from Francis Bach (available at http://cmm.ensmp.fr/~bach/kernel-ica/index.htm). The derivative is com)

曹希仁的Stochastic Learning and Optimization课件，绝对经典(The Stochastic Learning and Optimization Xiren Cao courseware, absolute classic)