ccsystems2e_Chapter05

matlab code for cryptography

此程序用提升法实现第二代小波变换 ％％ 我用的是非整数阶小波变换 ％％ 采用时域实现,步骤先列后行 ％％ 正变换：分裂，预测，更新； ％％ 反变换：更新，预测，合并 ％％ 只做一层（可以多层，而且每层的预测和更新方程不同）(the procedure used to upgrade method is the second generation wavelet transform%% I use a non-integer-order wavelet transform%% using time domain achieved, steps out first go backward%% transformation : split and forecast updates; %% inverse transformation : Update, forecasting, %% off with a layer (which can be multi-storey, but the typical forecast and updating different equation))

Dijkstra shortest path algorithm universal Matlab program

说明：  用来学习贝叶斯网络，包含结构学习和参数学习(For learning Bayesian networks, including structure learning and parameter learning)

Codes for Histogram_Equalization Algorithm, without using matlab embedded function.

说明：  切比雪夫-2型低通滤波器设计 基于matlab仿真编程的(-2 Chebyshev low-pass filter design based on Matlab simulation programming)

Comme les autres régimes d’états liés présentés précédemment, l’état de quatre impulsions est très sensible à l’énergie de saturation et au gain petit signal. Quand l’énergie de saturation (respectivement le gain petit signal) varie dans la gamme de quelques pJ

Average interpolation using cubic B-spline wavelet.

一些有关ga的论文 都是精心收集的 包含多个方向(Some papers are concerned ga carefully collected)

Algorithm Given are P training pairs {X1,d1,X2,d2....Xp,dp}, where Xi is (n*1) di is (n*1) No of Categories=R. i=1,2,...P Yi= Augmented input pattern( obtained by appending 1 to the input vector) i=1,2,…P In the following, k denotes the training step and p denotes the step counter within the training cycle Step 1: c>0 , Emin is chosen, Step 2: Weights are initialized at w at small values, w is (n+1)*1. Counters and error are initialized. k=1,p=1,E=0 Step 3: The training cycle begins here. Input is presented and output computed: Y=Yp, d=dp Oi=f(wtY) for i=1,2,….R Step 4: Weights are updated: wi=wi+1/2c(di-oi)Y for i=1,2,…..R. Step 5: Cycle error is computed: E=1/2(di-oi)2+E for i=1,2,…..R. Step 6: If p<P then p=p+1,k=k+1, and go to Step 3: Otherwise go to Step 7. Step 7: The training cycle is completed. For E=0,terminate the training session. Outputs weights and k. If E>0,then E=0 ,p=1, and enter the new training cycle by going to step 3.