neural-MLP-classifier

Bayesian Surprise Toolkit for Matlab T. Nathan Mundhenk, Laurent Itti(Bayesian Surprise Toolkit for Matlab T. Na than Mundhenk, Laurent Itti)

《matlab时尚编程百例》work70-1()

这个程序对简单LPC-10声码器做了一些改进，使其对浊音的激励变得较强，使用声激励LPC声码器来提高声音的质量。它与简单LPC-10音码器的差异是用激励检测器替换了基音检测器。(This program simply LPC-10 vocoder made some improvements to make them become a strong incentive voiced the use of acoustic excitation LPC vocoder to improve the sound quality. It sounds simple LPC-10 codec difference is replaced with the excitation detector pitch detector.)

设r是f(x) = 0的根，选取x0作为r初始近似值，过点（x0,f(x0)）做曲线y = f(x)的切线L，L的方程为y = f(x0)+f (x0)(x-x0)，求出L与x轴交点的横坐标 x1 = x0-f(x0)/f (x0)，称x1为r的一次近似值。过点（x1,f(x1)）做曲线y = f(x)的切线，并求该切线与x轴交点的横坐标 x2 = x1-f(x1)/f (x1)，称x2为r的二次近似值。重复以上过程，得r的近似值序列，其中x(n+1)=x(n)－f(x(n))/f (x(n))，称为r的n+1次近似值(Let r is f (x) = 0 root, select the initial approximation x0 as the r, over point (x0, f (x0)) to do the curve y = f (x) the tangent L, L the equation y = f ( x0)+ f ' (x0) (x-x0), find the intersection of L and the x-axis of abscissa x1 = x0-f (x0)/f' (x0), x1 is called an approximation r. Through points (x1, f (x1)) to do the curve y = f (x) the tangent, and find the intersection of the tangent with the x-axis of abscissa x2 = x1-f (x1)/f ' (x1), x2 is called r the second approximation. Repeat the process, get an approximation of the sequence r, where x (n+1) = x (n)-f (x (n))/f ' (x (n)), as an approximation of r n+1 times)

说明：  室内可见光通信系统信道模型，用于室内可见光通信系统基础信道估计仿真实验(Channel Model of Indoor Visible Communication System for Basic Channel Estimation of Indoor Visible Communication System)

VC和MATLAB的混合编程，提供VC和MATLAB的高效运作。VC的界面设计，MATLAB的强大计算功能。(Mixture of VC and MATLAB programming and provide the efficient operation of the VC and MATLAB. VC interface design, MATLAB' s powerful computing capabilities.)

Scheduling code for cluster

air-to-ground missile model

四边简支的复合材料层合板基于遗传算法的的最佳铺层顺序求解(Solution of the Best Laying Sequence of Simply Supported Composite Laminates Based on Genetic Algorithms)

RESTORATION OF GENERATED NOISY IMAGES BY USING FILTERS : Arithmetic Mean, Median, Max, Min,Mid Point Filtering