permutes

LBM方法求解流体力学中的lid driven flow 问题。输入迭代次数，输出流场速度矢量分布图。(LBM method for solving fluid mechanics in the lid driven flow problems. Enter the number of iterations, the output flow velocity vector distribution.)

In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discretedata linear filtering problem [Kalman60]. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. A very “friendly” introduction to the general idea of the Kalman filter can be found in Chapter 1 of [Maybeck79], while a more complete introductory discussion can be found in [Sorenson70], which also contains some interesting historical narrative.(In 1960, R. E. Kalman published his famous paper describ ing a recursive solution to the discretedata li near filtering problem [Kalman60]. Since that time, due in large part to advances in digital computi Vi, the Kalman filter has been the subject of extens ive research and application. particularly in the area of autonomous or assis ted navigation. A very "friendly" introductio n to the general idea of the Kalman filter can be f ound in Chapter 1 of [Maybeck79] while a more complete introductory discussion can be found in [Sorenson70] which also contains some interesting historic al narrative.)

《MATLAB神经网络30个案例分析》中的第6个例子，案例6 PID神经元网络解耦控制算法_多变量系统控制。希望对大家有一定的帮助！(The MATLAB neural network analysis of 30 cases of 6 examples, case 6 _ multi-variable PID neural network decoupling control algorithm control system. Hope to have certain help to everybody! )

simulation 64point FFT using matlab

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.

非常适合计算机视觉方面的研究使用,数据模型归一化，模态振动，有小波分析的盲信号处理。( Very suitable for the study using computer vision, Normalized data model, modal vibration, There Wavelet Analysis Blind Signal Processing.)

CCNA Practice Questions (Exam 640-802).This book is to provide you with practice questions complete with answers and explanations that will help you learn, drill, and review for the 640-802, 640-822,and 640-816 certification exams. The book offers a large number of questions to practice each exam objective and will help you assess your knowledge before you write the real exam.

利用PLSI算法自动对一个文档集进行聚类，需要调节聚类的维度K值，从而得到最大的likelihood(PLSI automatically using an algorithm for clustering document set needs to be adjusted to the dimension value of K clusters, to obtain the maximum likelihood)

合成孔径雷达的自聚焦算法使用matlab实现。是最大对比度法(Synthetic aperture radar autofocus algorithm using matlab implementation. Is the maximum contrast method)

滑模变结构控制的MATLAB仿真实例之一 滑模变结构控制的MATLAB仿真实例之一(Sliding Mode Variable Structure Control of the MATLAB simulation of sliding mode variable structure control, one of the MATLAB simulation)