risk456_2

几种dpsk mpsk调制编码算法的比特错误比率分析(Several dpsk mpsk modulation coding bit error ratio analysis)

machine data processing part2

该程序用matlab实现FLMS算法，并绘出了收敛曲线。(The realization of the program FLMS algorithm with matlab, and the convergence curve is drawn.)

用MATLAB编写的递推最小二乘算法算法LMS算法,karlman算法(using MATLAB recursive least squares algorithm LMS algorithm, the algorithm karlman)

基于循环移位寄存器的ldpc编码的仿真程序,好程序啊,(cycle shift register on the ldpc coded simulation program, the program ah,)

小波差值与构造，三个matlab程序，希望对大家有用(Wavelet difference with the structure, three matlab procedures useful for everyone)

This is Gaussian elimination and Lagrange interpolation programs in matlab

％ [BestPop,Trace]=fmaxga(FUN,LB,UB,eranum,popsize,pcross,pmutation) ％ Finds a maximum of a function of several variables. ％ fmaxga solves problems of the form: ％ max F(X) subject to: LB <= X <= UB ％ BestPop--------最优的群体即为最优的染色体群 ％ Trace----------最佳染色体所对应的目标函数值 ％ FUN------------目标函数 ％ LB-------------自变量下限 ％ UB-------------自变量上限 ％ eranum---------种群的代数,取100--1000(默认1000) ％ popsize--------每一代种群的规模；此可取50--100(默认50) ％ pcross---------交叉的概率,此概率一般取0.5--0.85之间较好(默认0.8) ％ pmutation------变异的概率,该概率一般取0.05-0.2左右较好(默认0.1) ％ options--------1×2矩阵,options(1)=0二进制编码(默认0),option(1)~=0十进制编码,option(2)设定求解精度(默认1e-4)( [BestPop, Trace] = fmaxga (FUN, LB, UB, eranum, popsize, pcross, pmutation) Finds a maximum of a function of several variables. Fmaxga solves problems of the form: max F (X) subject to : LB <= X <= UB BestPop-------- optimal chromosome groups is the best group Trace---------- chromosome corresponding to the best objective function value FUN------------ objective function LB------------- variable lower limit since the UB------------- variable upper limit eranum--------- populations algebra, take 100- 1000 (default 1000) popsize-------- population size of each generation this desirable 50- 100 (default 50) pcross--------- crossover probability, the probability of a general check 0.5- 0.85 between the better (default 0.8) pmutation------ mutation probability, the probability of 0.05 general admission better about-0.2 (default 0.1) options-------- 1 × 2 matrix, options (1) = 0 binary code (default 0), option (1) ~ = 0 decimal coding, option (2 ) set accuracy (default 1e-4))

The new regular expression library version

独立成份分析（ＩＣＡ）以及ｗｉｎｎｅｒ滤波 Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S + N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...( Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S+ N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...)