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智能优化算法  神经网络预测 雷达通信 无线传感器

信号处理 图像处理 路径规划 元胞自动机 无人机 电力系统

⛄ 内容介绍

井下动液面是抽油井的一个重要生产参数,能够反映抽油井的生产效率.采用基于时间序列分析的方法对动液面进行预测,首先由最小二乘支持向量机(LSSVM)方法建立预测模型,对其中的正则化参数和核函数参数采用狮群算法(LSO)进行优化选取.实例验证了本文所提出算法的有效性.

⛄ 部分代码

function [out1,out2] = mapminmax(in1,in2,in3,in4)

%MAPMINMAX Map matrix row minimum and maximum values to [-1 1].

%  

%  Syntax

%

%   [y,ps] = mapminmax(x,ymin,ymax)

%   [y,ps] = mapminmax(x,fp)

%   y = mapminmax('apply',x,ps)

%   x = mapminmax('reverse',y,ps)

%   dx_dy = mapminmax('dx',x,y,ps)

%   dx_dy = mapminmax('dx',x,[],ps)

%   name = mapminmax('name');

%   fp = mapminmax('pdefaults');

%   names = mapminmax('pnames');

%   mapminmax('pcheck', fp);

%

%  Description

%  

%   MAPMINMAX processes matrices by normalizing the minimum and maximum values

%   of each row to [YMIN, YMAX].

%  

%   MAPMINMAX(X,YMIN,YMAX) takes X and optional parameters,

%     X - NxQ matrix or a 1xTS row cell array of NxQ matrices.

%     YMIN - Minimum value for each row of Y. (Default is -1)

%     YMAX - Maximum value for each row of Y. (Default is +1)

%   and returns,

%     Y - Each MxQ matrix (where M == N) (optional).

%     PS - Process settings, to allow consistent processing of values.

%

%   MAPMINMAX(X,FP) takes parameters as struct: FP.ymin, FP.ymax.

%   MAPMINMAX('apply',X,PS) returns Y, given X and settings PS.

%   MAPMINMAX('reverse',Y,PS) returns X, given Y and settings PS.

%   MAPMINMAX('dx',X,Y,PS) returns MxNxQ derivative of Y w/respect to X.

%   MAPMINMAX('dx',X,[],PS)  returns the derivative, less efficiently.

%   MAPMINMAX('name') returns the name of this process method.

%   MAPMINMAX('pdefaults') returns default process parameter structure.

%   MAPMINMAX('pdesc') returns the process parameter descriptions.

%   MAPMINMAX('pcheck',fp) throws an error if any parameter is illegal.

%    

% Examples

%

%   Here is how to format a matrix so that the minimum and maximum

%   values of each row are mapped to default interval [-1,+1].

%

%     x1 = [1 2 4; 1 1 1; 3 2 2; 0 0 0]

%     [y1,ps] = mapminmax(x1)

%

%   Next, we apply the same processing settings to new values.

%

%     x2 = [5 2 3; 1 1 1; 6 7 3; 0 0 0]

%     y2 = mapminmax('apply',x2,ps)

%

%   Here we reverse the processing of y1 to get x1 again.

%

%     x1_again = mapminmax('reverse',y1,ps)

%

%  Algorithm

%

%     It is assumed that X has only finite real values, and that

%     the elements of each row are not all equal.

%

%     y = (ymax-ymin)*(x-xmin)/(xmax-xmin) + ymin;

%

%  See also FIXUNKNOWNS, MAPSTD, PROCESSPCA, REMOVECONSTANTROWS


% Copyright 1992-2007 The MathWorks, Inc.

% $Revision: 1.1.6.11 $



% Process function boiler plate script

boiler_process


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Name

function n = name

n = 'Map Minimum and Maximum';


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Parameter Defaults

function fp = param_defaults(values)


if length(values)>=1, fp.ymin = values{1}; else fp.ymin = -1; end

if length(values)>=2, fp.ymax = values{2}; else fp.ymax = fp.ymin + 2; end


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Parameter Names

function names = param_names()

names = {'Mininum value for each row of Y.', 'Maximum value for each row of Y.'};


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Parameter Check

function err = param_check(fp)


mn = fp.ymin;

mx = fp.ymax;

if ~isa(mn,'double') || any(size(mn)~=[1 1]) || ~isreal(mn) || ~isfinite(mn)

  err = 'ymin must be a real scalar value.';

elseif ~isa(mx,'double') || any(size(mx)~=[1 1]) || ~isreal(mx) || ~isfinite(mx) || (mx <= mn)

  err = 'ymax must be a real scalar value greater than ymin.';

else

  err = '';

end


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% New Process

function [y,ps] = new_process(x,fp)


% Replace NaN with finite values in same row

rows = size(x,1);

for i=1:rows

  finiteInd = find(full(~isnan(x(i,:))),1);

  if isempty(finiteInd)

    xfinite = 0;

  else

    xfinite = x(finiteInd);

  end

  nanInd = isnan(x(i,:));

  x(i,nanInd) = xfinite;

end


ps.name = 'mapminmax';

ps.xrows = size(x,1);

ps.xmax = max(x,[],2);

ps.xmin = min(x,[],2);

ps.xrange = ps.xmax-ps.xmin;

ps.yrows = ps.xrows;

ps.ymax = fp.ymax;

ps.ymin = fp.ymin;

ps.yrange = ps.ymax-ps.ymin;


if any(ps.xmax == ps.xmin)

  warning('NNET:Processing','Use REMOVECONSTANTROWS to remove rows with constant values.');

end


y = apply_process(x,ps);


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Apply Process

function y = apply_process(x,ps)


Q = size(x,2);

oneQ = ones(1,Q);

rangex = ps.xmax-ps.xmin;

rangex(rangex==0) = 1; % Avoid divisions by zero

rangey = ps.ymax-ps.ymin;

y = rangey * (x-ps.xmin(:,oneQ))./rangex(:,oneQ) + ps.ymin;


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Reverse Process

function x = reverse_process(y,ps)


Q = size(y,2);

oneQ = ones(1,Q);

rangex = ps.xmax-ps.xmin;

rangey = ps.ymax-ps.ymin;

x = rangex(:,oneQ) .* (y-ps.ymin)*(1/rangey) + ps.xmin(:,oneQ);


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Derivative of Y w/respect to X

function dy_dx = derivative(x,y,ps);


Q = size(x,2);

rangex = ps.xmax-ps.xmin;

rangey = ps.ymax-ps.ymin;

d = diag(rangey ./ rangex);

dy_dx = d(:,:,ones(1,Q));


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Derivative of Y w/respect to X

function dx_dy = reverse_derivative(x,y,ps);


Q = size(x,2);

rangex = ps.xmax-ps.xmin;

rangey = ps.ymax-ps.ymin;

d = diag(rangex ./ rangey);

dx_dy = d(:,:,ones(1,Q));

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

function p = simulink_params(ps)


p = ...

  { ...

  'xmin',mat2str(ps.xmin);

  'xmax',mat2str(ps.xmax);

  'ymin',mat2str(ps.ymin);

  'ymax',mat2str(ps.ymax);

  };


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

function p = simulink_reverse_params(ps)


p = ...

  { ...

  'xmin',mat2str(ps.xmin);

  'xmax',mat2str(ps.xmax);

  'ymin',mat2str(ps.ymin);

  'ymax',mat2str(ps.ymax);

  };

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

⛄ 运行结果

⛄ 参考文献

[1]刘云, 易松. 基于双参数最小二乘支持向量机(TPA-LSSVM)的风电时间序列预测模型的优化研究[J]. 北京化工大学学报：自然科学版, 2019, 46(2):6.

[2]李雪竹, 陈国龙. 基于智能最小二乘支持向量机的大数据分析与预测[J]. 计算机工程, 2015.

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