【SVM预测】基于粒子群优化支持向量机实现预测matlab源码-优快云博客

一、简介

1 粒子群算法的概念
粒子群优化算法(PSO：Particle swarm optimization) 是一种进化计算技术(evolutionary computation)。源于对鸟群捕食的行为研究。粒子群优化算法的基本思想：是通过群体中个体之间的协作和信息共享来寻找最优解．
PSO的优势：在于简单容易实现并且没有许多参数的调节。目前已被广泛应用于函数优化、神经网络训练、模糊系统控制以及其他遗传算法的应用领域。

2 粒子群算法分析
2.1基本思想
粒子群算法通过设计一种无质量的粒子来模拟鸟群中的鸟，粒子仅具有两个属性：速度和位置，速度代表移动的快慢，位置代表移动的方向。每个粒子在搜索空间中单独的搜寻最优解，并将其记为当前个体极值，并将个体极值与整个粒子群里的其他粒子共享，找到最优的那个个体极值作为整个粒子群的当前全局最优解，粒子群中的所有粒子根据自己找到的当前个体极值和整个粒子群共享的当前全局最优解来调整自己的速度和位置。下面的动图很形象地展示了PSO算法的过程：【SVM预测】基于粒子群优化支持向量机实现预测matlab源码_matlab
2 更新规则
PSO初始化为一群随机粒子(随机解)。然后通过迭代找到最优解。在每一次的迭代中，粒子通过跟踪两个“极值”(pbest，gbest)来更新自己。在找到这两个最优值后，粒子通过下面的公式来更新自己的速度和位置。【SVM预测】基于粒子群优化支持向量机实现预测matlab源码_matlab_02
公式(1)的第一部分称为【记忆项】，表示上次速度大小和方向的影响；公式(1)的第二部分称为【自身认知项】，是从当前点指向粒子自身最好点的一个矢量，表示粒子的动作来源于自己经验的部分；公式(1)的第三部分称为【群体认知项】，是一个从当前点指向种群最好点的矢量，反映了粒子间的协同合作和知识共享。粒子就是通过自己的经验和同伴中最好的经验来决定下一步的运动。以上面两个公式为基础，形成了PSO的标准形式。【SVM预测】基于粒子群优化支持向量机实现预测matlab源码_matlab_03
公式(2)和公式(3)被视为标准PSO算法。
3 PSO算法的流程和伪代码

二、源代码

%该程序已在MATLAB2014a运行通过
 
function varargout = gui(varargin)
% GUI MATLAB code for gui.fig
%      GUI, by itself, creates a new GUI or raises the existing
%      singleton*.
%
%      H = GUI returns the handle to a new GUI or the handle to
%      the existing singleton*.
%
%      GUI('CALLBACK',hObject,eventData,handles,...) calls the local
%      function named CALLBACK in GUI.M with the given input arguments.
%
%      GUI('Property','Value',...) creates a new GUI or raises the
%      existing singleton*.  Starting from the left, property value pairs are
%      applied to the GUI before gui_OpeningFcn gets called.  An
%      unrecognized property name or invalid value makes property application
%      stop.  All inputs are passed to gui_OpeningFcn via varargin.
%
%      *See GUI Options on GUIDE's Tools menu.  Choose "GUI allows only one
%      instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
 
% Edit the above text to modify the response to help gui
 
% Last Modified by GUIDE v2.5 13-Sep-2015 19:51:50
 
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
                   'gui_Singleton',  gui_Singleton, ...
                   'gui_OpeningFcn', @gui_OpeningFcn, ...
                   'gui_OutputFcn',  @gui_OutputFcn, ...
                   'gui_LayoutFcn',  [] , ...
                   'gui_Callback',   []);
if nargin && ischar(varargin{1})
    gui_State.gui_Callback = str2func(varargin{1});
end
 
if nargout
    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
    gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
 
 
% --- Executes just before gui is made visible.
function gui_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject    handle to figure
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
% varargin   command line arguments to gui (see VARARGIN)
% Choose default command line output for gui
handles.output = hObject;
 
% Update handles structure
guidata(hObject, handles);
 
% UIWAIT makes gui wait for user response (see UIRESUME)
% uiwait(handles.figure1);
 
 
% --- Outputs from this function are returned to the command line.
function varargout = gui_OutputFcn(hObject, eventdata, handles) 
% varargout  cell array for returning output args (see VARARGOUT);
% hObject    handle to figure
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
 
% Get default command line output from handles structure
varargout{1} = handles.output;
 
 
 
function edit1_Callback(hObject, eventdata, handles)
% hObject    handle to edit1 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
 
% Hints: get(hObject,'String') returns contents of edit1 as text
%        str2double(get(hObject,'String')) returns contents of edit1 as a double
 
input = get(handles.edit1,'String'); 
input = str2num(input);
 
% --- Executes during object creation, after setting all properties.
function edit1_CreateFcn(hObject, eventdata, handles)
% hObject    handle to edit1 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called
 
% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end
 
 
 
function edit2_Callback(hObject, eventdata, handles)
% hObject    handle to edit2 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
 
% Hints: get(hObject,'String') returns contents of edit2 as text
%        str2double(get(hObject,'String')) returns contents of edit2 as a doubl
% --- Executes during object creation, after setting all properties.
input = get(handles.edit2,'String'); 
input = str2num(input);
 
 
function edit2_CreateFcn(hObject, eventdata, handles)
% hObject    handle to edit2 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called
 
% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end
 
 
 
function edit3_Callback(hObject, eventdata, handles)
% hObject    handle to edit3 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
 
% Hints: get(hObject,'String') returns contents of edit3 as text
%        str2double(get(hObject,'String')) returns contents of edit3 as a double
% --- Executes during object creation, after setting all properties.
input = get(handles.edit3,'String'); 
input = str2num(input);
 
function edit3_CreateFcn(hObject, eventdata, handles)
% hObject    handle to edit3 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called
 
% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end
 
 
% --- Executes on button press in pushbutton1.
function pushbutton1_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton1 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
C = 30;
theta = 2;%C为最小二乘支持向量机的正则化参数，theta为高斯径向基的核函数参数，两个需要进行优化选择调试
NumOfPre = 1;%预测天数，在此预测本季度最后七天
Time = 24;
Data = xlsread('a23.xls');%此为从excel表格读数据的命令，表示将表格的数据读到Data数组中，省略表格中的第一行第一列文字部分 可输入你要预测的表格名称
[M N] = size(Data);%计算读入数据的行和列 M行N列
for i = 1:3
    maxData = max(Data(:,i));
    minData = min(Data(:,i));
    Data1(:,i) = (Data(:,i) - minData)/(maxData-minData);%对温度进行归一化处理
end
for i = 4:5
    Data1(:,i) = Data(:,i);
end
for i = 6:N
    Data1(:,i) = log10(Data(:,i)) ;%对负荷进行对数处理 温度和负荷的预处理 可采用不同的方法 
end
Dim =  M - 2 - NumOfPre;%训练样本数%训练样本数
Input = zeros(M-2,12,Time);%预先分配处理后的输入向量空间
y = zeros(Dim,Time);
for i = 3:M 
    for j = 1:Time
        %%选取前一天温度、同一时刻的负荷，前两天的负荷，当天的温度作为输入特征
        x = [Data1(i-1,1:5), Data1(i-1,j+5), Data1(i-2,j+5),Data1(i,1:5)];
        Input(i-2,:,j) = x;
        y(i-2,j) = Data1(i,j+5);
    end
end
Dist = zeros(Dim,Dim,Time);%预先分配距离空间
for i=1:Time
    for j=1:Dim
        for k=1:Dim
            Dist(j,k,i) = (Input(j,:,i) - Input(k,:,i))*(Input(j,:,i) - Input(k,:,i))';
        end
    end
end
Dist1 = exp(-Dist/(2*theta));%RBF
for i=1:Time
    H = Dist1(:,:,i) + eye(Dim)/C;%最小二乘支持向量的H矩阵
    f = -y(1:Dim,i); 
    Aeq = ones(Dim,1)';
    beq = [0];
    option.MaxIter=1000;
    [a,fval]=quadprog(H,f,[],[],Aeq,beq);%,[],[],[],option);
    b = 0;
    for j = 1:Dim
        b(j) = y(j,i) - a(j)/C - a'* Dist1(:,j,i);%求每个输入特征对应的b
    end
    b = sum(b)/Dim;%求平均b，消除误差
    for j = Dim + 1:M-2
        for k = 1:Dim
            K(k) = exp(-(Input(j,:,i) - Input(k,:,i))*(Input(j,:,i) - Input(k,:,i))'/(2*theta));%预测输入特征与训练特征的RBF距离
        end
        Pre(j-Dim,i) = sum(a'*K') + b;  %求解预测值   
    end
end
Len = M  - (Dim + 3) + 1;%预测的天数 取本季度最后Len天
Pre = 10.^Pre;
for i = 1:Len
    %figure 
    axes(handles.axes1);
    plot(1:Time,Data(i+Dim+2,6:N),'-or',1:Time,Pre(i,:),'-vk');%画出每一天的预测值和真实值
    hold on
    scatter(1:Time,Data(i+Dim+2,6:N),'o')
    scatter(1:Time,Pre(i,:),'v')
    legend('实际值','预测值','location','southeast')
    hold off
end
Acu = (Pre - Data(Dim+3:M,6:N))./Data(Dim+3:M,6:N);%相对误差
save Acu.mat Acu
s=0;
for i=1:Time
    s=abs(Acu(1,i))+s;
end
acu=s/Time;
save acu.mat acu;
Result=[C,theta,acu];
disp(Result);