"Matlab常用均值生成函数预测算法及程序详解"

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The given summary contains information about common algorithms used in MATLAB, including the time series prediction algorithm. It involves three main programs: predict_fun.m, timeseries.m, and seriesexpan.m. The predict_fun.m is the main program that utilizes the timeseries.m and seriesexpan.m as subprograms. The predict_fun.m program takes input data "b" (a vector) and the number of prediction data "step", and outputs the prediction data "ima_pre" (also a vector). The predict_fun.m program starts by standardizing the input data "b" by subtracting the mean and dividing by the standard deviation. Then, it invokes the timeseries.m program to process the standardized data and generate a resultant sequence "f" along with another sequence "x". Next, the program calls the seriesexpan.m program to further process the original data "b" and obtain "old_f2" - an expanded series of data based on the original input. Finally, the program fills in negative values in the sequence "f" with a threshold of 0.0001. Overall, the predict_fun.m algorithm is designed to take a set of training data, process it using the timeseries.m and seriesexpan.m subprograms, and generate a prediction sequence. The algorithm is useful for time series forecasting and can be applied to various fields such as finance, economics, and weather forecasting.

k=inf;

for i=1:row

if (length(find(s_path==i))==0)&(k>distance(i))

k=distance(i);

temp_2=i;

end

s_path=[s_path,temp_2];

temp=temp_2;

end

%output the result

path(1)=endpoint;

i=1;

while path(i)~=start

path(i+1)=flag(path(i));

i=i+1;

end

path(i)=start;

path=path(end:-1:1);

short_distance=distance(endpoint);

三绘制差分方程的映射分叉图

function fork1(a);

% 绘制 x_(n+1)=1-a*x^2_n 映射的分叉图

% Example:

% fork1([0,2]);

N=300; % 取样点数

A=linspace(a(1),a(2),N);

starx=0.9;

Z=[];

h=waitbar(0,'please wait');m=1;

for ap=A;

x=starx;

for k=1:50;

x=1-ap*x^2;

end

for k=1:201;

x=1-ap*x^2;

Z=[Z,ap-x*i];

end

waitbar(m/N,h,['completed ',num2str(round(100*m/N)),'%'],h);

m=m+1;

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