BP预测基于鸡群算法改进BP神经网络实现数据预测

Posted 2021-08-08 博主企鹅号1575304183

 一、 BP神经网络预测算法简介

说明：1.1节主要是概括和帮助理解考虑影响因素的BP神经网络算法原理，即常规的BP模型训练原理讲解（可根据自身掌握的知识是否跳过）。1.2节开始讲基于历史值影响的BP神经网络预测模型。

使用BP神经网络进行预测时，从考虑的输入指标角度，主要有两类模型：

1.1 受相关指标影响的BP神经网络算法原理

如图一所示，使用MATLAB的newff函数训练BP时，可以看到大部分情况是三层的神经网络（即输入层，隐含层，输出层）。这里帮助理解下神经网络原理：
1）输入层：相当于人的五官，五官获取外部信息，对应神经网络模型input端口接收输入数据的过程。
2）隐含层：对应人的大脑，大脑对五官传递来的数据进行分析和思考，神经网络的隐含层hidden Layer对输入层传来的数据x进行映射，简单理解为一个公式hiddenLayer_output=F(w*x+b)。其中，w、b叫做权重、阈值参数，F()为映射规则，也叫激活函数，hiddenLayer_output是隐含层对于传来的数据映射的输出值。换句话说，隐含层对于输入的影响因素数据x进行了映射，产生了映射值。
3）输出层：可以对应为人的四肢，大脑对五官传来的信息经过思考（隐含层映射）之后，再控制四肢执行动作（向外部作出响应）。类似地，BP神经网络的输出层对hiddenLayer_output再次进行映射，outputLayer_output=w *hiddenLayer_output+b。其中，w、b为权重、阈值参数，outputLayer_output是神经网络输出层的输出值（也叫仿真值、预测值）（理解为，人脑对外的执行动作，比如婴儿拍打桌子）。
4）梯度下降算法：通过计算outputLayer_output和神经网络模型传入的y值之间的偏差，使用算法来相应调整权重和阈值等参数。这个过程，可以理解为婴儿拍打桌子，打偏了，根据偏离的距离远近，来调整身体使得再次挥动的胳膊不断靠近桌子，最终打中。

再举个例子来加深理解：

图一所示BP神经网络，具备输入层、隐含层和输出层。BP是如何通过这三层结构来实现输出层的输出值outputLayer_output，不断逼近给定的y值，从而训练得到一个精准的模型的呢？

从图中串起来的端口，可以想到一个过程：坐地铁，将图一想象为一条地铁线路。王某某坐地铁回家的一天：在input起点站上车，中途经过了很多站（hiddenLayer），然后发现坐过头了（outputLayer对应现在的位置），那么王某某将会根据现在的位置离家（目标Target）的距离（误差Error），返回到中途的地铁站（hiddenLayer）重新坐地铁（误差反向传递，使用梯度下降算法更新w和b），如果王某某又一次发生失误，那么将再次进行这个调整的过程。

从在婴儿拍打桌子和王某某坐地铁的例子中，思考问题：BP的完整训练，需要先传入数据给input，再经过隐含层的映射，输出层得到BP仿真值，根据仿真值与目标值的误差，来调整参数，使得仿真值不断逼近目标值。比如（1）婴儿受到了外界的干扰因素（x），从而作出反应拍桌（predict），大脑不断的调整胳膊位置，控制四肢拍准（y、Target）。（2）王某某上车点（x），过站点（predict），不断返回中途站来调整位置，到家（y、Target）。

在这些环节中，涉及了影响因素数据x，目标值数据y（Target）。根据x，y，使用BP算法来寻求x与y之间存在的规律，实现由x来映射逼近y，这就是BP神经网络算法的作用。再多说一句，上述讲的过程，都是BP模型训练，那么最终得到的模型虽然训练准确，但是找到的规律（bp network）是否准确与可靠呢。于是，我们再给x1到训练好的bp network中，得到相应的BP输出值（预测值）predict1，通过作图，计算Mse，Mape，R方等指标，来对比predict1和y1的接近程度，就可以知道模型是否预测准确。这是BP模型的测试过程，即实现对数据的预测，并且对比实际值检验预测是否准确。

图一 3层BP神经网络结构图

1.2 基于历史值影响的BP神经网络

以电力负荷预测问题为例，进行两种模型的区分。在预测某个时间段内的电力负荷时：

一种做法，是考虑 t 时刻的气候因素指标，比如该时刻的空气湿度x1，温度x2，以及节假日x3等的影响，对 t 时刻的负荷值进行预测。这是前面1.1所说的模型。

另一种做法，是认为电力负荷值的变化，与时间相关，比如认为t-1，t-2，t-3时刻的电力负荷值与t时刻的负荷值有关系，即满足公式y(t)=F(y(t-1),y(t-2),y(t-3))。采用BP神经网络进行训练模型时，则输入到神经网络的影响因素值为历史负荷值y(t-1),y(t-2),y(t-3)，特别地，3叫做自回归阶数或者延迟。给到神经网络中的目标输出值为y(t)。

 二、鸡群算法

 

 

 2.1 公鸡位置更新

2.2 母鸡位置更新 

 

 2.3 小鸡位置更新

 

 

三、部分代码

% -------------------------------------------------------------------------
% Chicken Swarm Optimization (CSO) (demo)
% Programmed by Xian-Bing Meng
% Updated at Jun 21, 2015.    
% Email: [e-mail]
%
% This is a simple demo version only implemented the basic idea of CSO for        
% solving the unconstrained problem, namely Sphere function.    
% The details about CSO are illustratred in the following paper.                                                
% Xian-Bing Meng, et al. A new bio-inspired algorithm: Chicken Swarm 
%    Optimization. The Fifth International Conference on Swarm Intelligence 
%
% The parameters in CSO are presented as follows.
% FitFunc    % The objective function
% M          % Maxmimal generations (iterations)
% pop        % Population size
% dim        % Dimension
% G          % How often the chicken swarm can be updated.
% rPercent   % The population size of roosters accounts for "rPercent" 
%   percent of the total population size
% hPercent   % The population size of hens accounts for "hPercent" percent 
%  of the total population size
% mPercent   % The population size of mother hens accounts for "mPercent" 
%  percent of the population size of hens
%
% Using the default value,CSO can be executed using the following code.
% [ bestX, fMin ] = CSO
% -------------------------------------------------------------------------
 
%*************************************************************************
% Revision 1
% Revised at May 23, 2015
% 1.Note that the previous version of CSO doen't consider the situation 
%   that there maybe not exist hens in a group. 
%   We assume there exist at least one hen in each group.

% Revision 2
% Revised at Jun 24, 2015
% 1.Correct an error at line "100".
%*************************************************************************

% Main programs

function [ bestX, fMin ] = CSO( FitFunc, M, pop, dim, G, rPercent, hPercent, mPercent )
% Display help
help CSO.m
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% set the default parameters
if nargin < 1
    FitFunc = @Sphere;
    M = 1000;  
    pop = 100;  
    dim = 20;  
    G = 10;       
    rPercent = 0.15; 
    hPercent = 0.7;  
    mPercent = 0.5;                  
end

rNum = round( pop * rPercent );    % The population size of roosters
hNum = round( pop * hPercent );    % The population size of hens
cNum = pop - rNum - hNum;          % The population size of chicks
mNum = round( hNum * mPercent );   % The population size of mother hens

lb= -100*ones( 1,dim );   % Lower bounds
ub= 100*ones( 1,dim );    % Upper bounds

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Initialization
for i = 1 : pop
    x( i, : ) = lb + (ub - lb) .* rand( 1, dim ); 
    fit( i ) = FitFunc( x( i, : ) ); 
end
pFit = fit; % The individual's best fitness value
pX = x;     % The individual's best position corresponding to the pFit

[ fMin, bestIndex ] = min( fit );        % fMin denotes the global optimum
% bestX denotes the position corresponding to fMin
bestX = x( bestIndex, : );   

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Start the iteration.
 
for t = 1 : M
    
    % This parameter is to describe how closely the chicks would follow 
    % their mother to forage for food. In fact, there exist cNum chicks, 
    % thus only cNum values of FL would be used. 
    FL = rand( pop, 1 ) .* 0.4 + 0.5;  
    
    % The chicken swarm'status about hierarchal order, dominance  
    % relationship, mother-child relationship, the roosters, hens and the 
    % chicks in a group will remain stable during a period of time. These  
    % statuses can be updated every several (G) time steps.The parameter G 
    % is used to simulate the situation that the chicken swarm have been  
    % changed, including some chickens have died, or the chicks have grown 
    % up and became roosters or hens, some mother hens have hatched new
    % offspring (chicks) and so on.
   
   if mod( t, G ) == 1 || t == 1   
                
        [ ans, sortIndex ] = sort( pFit );   
        % How the chicken swarm can be divided into groups and the identity
        % of the chickens (roosters, hens and chicks) can be determined all 
        % depend on the fitness values of the chickens themselves. Hence we 
        % use sortIndex(i) to describe the chicken, not the index i itself.
        
        motherLib = randperm( hNum, mNum ) + rNum;   
        % Randomly select mNum hens which would be the mother hens.
        % We assume that all roosters are stronger than the hens, likewise, 
        % hens are stronger than the chicks.In CSO, the strong is reflected
        % by the good fitness value. Here, the optimization problems is 
        % minimal ones, thus the more strong ones correspond to the ones  
        % with lower fitness values.
  
        % Given the fact the 1 : rNum chickens' fitness values maybe not
        % the best rNum ones. Thus we use sortIndex( 1 : rNum ) to describe
        % the roosters. In turn, sortIndex( (rNum + 1) :(rNum + 1 + hNum ))
        % to describle the mother hens, .....chicks.

        % Here motherLib include all the mother hens. 
      
       %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
        % Randomly select each hen's mate, rooster. Hence we can determine
        % which group each hen inhabits using "mate".Each rooster stands
        % for a group.For simplicity, we assume that there exist only one
        % rooster and at least one hen in each group.
        mate = randpermF( rNum, hNum );
        
        % Randomly select cNum chicks' mother hens
        mother = motherLib( randi( mNum, cNum, 1 ) );  
   end
    
   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%   
   for i = 1 : rNum                % Update the rNum roosters' values.
        
        % randomly select another rooster different from the i (th) one.
        anotherRooster = randiTabu( 1, rNum, i, 1 );  
        if( pFit( sortIndex( i ) ) <= pFit( sortIndex( anotherRooster ) ) )
            tempSigma = 1;
        else
            tempSigma = exp( ( pFit( sortIndex( anotherRooster ) ) - ...
                pFit( sortIndex( i ) ) ) / ( abs( pFit( sortIndex(i) ) )...
                + realmin ) );
        end
        
        x( sortIndex( i ), : ) = pX( sortIndex( i ), : ) .* ( 1 + ...
            tempSigma .* randn( 1, dim ) );
        x( sortIndex( i ), : ) = Bounds( x( sortIndex( i ), : ), lb, ub );
        fit( sortIndex( i ) ) = FitFunc( x( sortIndex( i ), : ) );
    end
    
    for i = ( rNum + 1 ) : ( rNum + hNum )  % Update the hNum hens' values.
        
        other = randiTabu( 1,  i,  mate( i - rNum ), 1 );  
        % randomly select another chicken different from the i (th)  
        % chicken's mate. Note that the "other" chicken's fitness value  
        % should be superior to that of the i (th) chicken. This means the   
        % i (th) chicken may steal the better food found by the "other" 
        % (th) chicken.
        
        c1 = exp( ( pFit( sortIndex( i ) ) - pFit( sortIndex( mate( i - ...
            rNum ) ) ) )/ ( abs( pFit( sortIndex(i) ) ) + realmin ) );
            
        c2 = exp( ( -pFit( sortIndex( i ) ) + pFit( sortIndex( other ) )));

        x( sortIndex( i ), : ) = pX( sortIndex( i ), : ) + ( pX(...
            sortIndex( mate( i - rNum ) ), : )- pX( sortIndex( i ), : ) )...
             .* c1 .* rand( 1, dim ) + ( pX( sortIndex( other ), : ) - ...
             pX( sortIndex( i ), : ) ) .* c2 .* rand( 1, dim ); 
        x( sortIndex( i ), : ) = Bounds( x( sortIndex( i ), : ), lb, ub );
        fit( sortIndex( i ) ) = FitFunc( x( sortIndex( i ), : ) );
    end
    
    for i = ( rNum + hNum + 1 ) : pop    % Update the cNum chicks' values.
        x( sortIndex( i ), : ) = pX( sortIndex( i ), : ) + ( pX( ...
            sortIndex( mother( i - rNum - hNum ) ), : ) - ...
            pX( sortIndex( i ), : ) ) .* FL( i );
        x( sortIndex( i ), : ) = Bounds( x( sortIndex( i ), : ), lb, ub );
        fit( sortIndex( i ) ) = FitFunc( x( sortIndex( i ), : ) );
    end
    
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
   % Update the individual's best fitness vlaue and the global best one
   
    for i = 1 : pop 
        if ( fit( i ) < pFit( i ) )
            pFit( i ) = fit( i );
            pX( i, : ) = x( i, : );
        end
        
        if( pFit( i ) < fMin )
            fMin = pFit( i );
            bestX = pX( i, : );
        end
    end
end
% End of the main program

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% The following functions are associated with the main program
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This function is the objective function
function y = Sphere( x )
y = sum( x .^ 2 );

% Application of simple limits/bounds
function s = Bounds( s, Lb, Ub)
  % Apply the lower bound vector
  temp = s;
  I = temp < Lb;
  temp(I) = Lb(I);
  
  % Apply the upper bound vector 
  J = temp > Ub;
  temp(J) = Ub(J);
  % Update this new move 
  s = temp;

%--------------------------------------------------------------------------
% This function generate "dim" values, all of which are different from
%  the value of "tabu"
function value = randiTabu( min, max, tabu, dim )
value = ones( dim, 1 ) .* max .* 2;
num = 1;
while ( num <= dim )
    temp = randi( [min, max], 1, 1 );
    if( length( find( value ~= temp ) ) == dim && temp ~= tabu )
        value( num ) = temp;
        num = num + 1;
    end
end

%--------------------------------------------------------------------------
function result = randpermF( range, dim )
% The original function "randperm" in Matlab is only confined to the
% situation that dimension is no bigger than dim. This function is 
% applied to solve that situation.

temp = randperm( range, range );
temp2 = randi( range, dim, 1 );
index = randperm( dim, ( dim - range ) );
result = [ temp, temp2( index )' ];

四、仿真结果

图2鸡群算法收敛曲线

测试统计如下表所示

测试结果	测试集正确率	训练集正确率
BP神经网络	100%	95%
CSO-BP	100%	99.8%

五、参考文献及代码私信博主

《基于BP神经网络的宁夏水资源需求量预测》