交通运输能力预测MATLAB仿真程序（完整）

function varargout = untitled1(varargin) % UNTITLED1 M-file for untitled1.fig % UNTITLED1, by itself, creates a new UNTITLED1 or raises the existing % singleton*. % % H = UNTITLED1 returns the handle to a new UNTITLED1 or the handle to % the existing singleton*. % % UNTITLED1('CALLBACK',hObject,eventData,handles,...) calls the local % function named CALLBACK in UNTITLED1.M with the given input arguments. % % UNTITLED1('Property','Value',...) creates a new UNTITLED1 or raises the % existing singleton*. Starting from the left, property value pairs are % applied to the GUI before untitled1_OpeningFcn gets called. An % unrecognized property name or invalid value makes property application % stop. All inputs are passed to untitled1_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 untitled1 % Last Modified by GUIDE v2.5 05-Jun-2017 23:33:18 % Begin initialization code - DO NOT EDIT gui_Singleton = 1; gui_State = struct('gui_Name', mfilename, ... 'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @untitled1_OpeningFcn, ... 'gui_OutputFcn', @untitled1_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 untitled1 is made visible. function untitled1_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 untitled1 (see VARARGIN) % Choose default command line output for untitled1 handles.output = hObject; % Update handles structure guidata(hObject, handles); % UIWAIT makes untitled1 wait for user response (see UIRESUME) % uiwait(handles.figure1); % --- Outputs from this function are returned to the command line. function varargout = untitled1_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; % --- Executes on selection change in day. function day_Callback(hObject, eventdata, handles) % hObject handle to day (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % Hints: contents = get(hObject,'String') returns day contents as cell array % contents{get(hObject,'Value')} returns selected item from day % --- Executes during object creation, after setting all properties. function day_CreateFcn(hObject, eventdata, handles) % hObject handle to day (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles empty - handles not created until after all CreateFcns called % Hint: popupmenu 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 data. function data_Callback(hObject, eventdata, handles) % hObject handle to data (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % --- Executes on button press in wangluo. function wangluo_Callback(hObject, eventdata, handles) % hObject handle to wangluo (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) p=[58478 135185 5.46 0.23 16.5 0.21 1005.2 585.44; 67884 152369 5.46 0.27 18.7 0.26 1105.6 575.03; 74462 182563 6.01 0.25 21.6 0.28 1204.6 601.23; 78345 201587 6.12 0.26 25.8 0.29 1316.5 627.89; 82067 225689 6.21 0.26 30.5 0.31 1423.5 676.95; 89403 240568 6.37 0.28 34.9 0.33 1536.2 716.32; 95933 263856 6.38 0.28 39.8 0.36 1632.6 765.24; 104790 285697 6.65 0.30 42.5 0.39 1753.2 812.22; 116694 308765 6.65 0.30 46.7 0.41 1865.5 875.26]'; t=[102569 52365 46251; 124587 60821 56245; 148792 69253 67362; 162568 79856 78165; 186592 91658 90548; 205862 99635 98758; 226598 109862 102564; 245636 120566 111257; 263595 130378 120356]'; a=[1 2 3 5 7 8]; P=p; for i=1:6 P(a(i),:)=(p(a(i),:)-min(p(a(i),:)))/(max(p(a(i),:))-min(p(a(i),:))); end for i=1:3 T(i,:)=(t(i,:)-min(t(i,:)))/(max(t(i,:))-min(t(i,:))); end P_train=[P(:,1) P(:,2) P(:,3) P(:,4) P(:,5) P(:,6) P(:,7)]; T_train=[T(:,1) T(:,2) T(:,3) T(:,4) T(:,5) T(:,6) T(:,7)]; P_test=[P(:,8) P(:,9)]; T_test=[T(:,8) T(:,9)]; for i=0.1:0.1:1 net=newgrnn(P_train,T_train,i); y_out=sim(net,P_train) y=sim(net,P_test) end % --- Executes on button press in yuce. function yuce_Callback(hObject, eventdata, handles) % hObject handle to yuce (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) p=[58478 135185 5.46 0.23 16.5 0.21 1005.2 585.44; 67884 152369 5.46 0.27 18.7 0.26 1105.6 575.03; 74462 182563 6.01 0.25 21.6 0.28 1204.6 601.23; 78345 201587 6.12 0.26 25.8 0.29 1316.5 627.89; 82067 225689 6.21 0.26 30.5 0.31 1423.5 676.95; 89403 240568 6.37 0.28 34.9 0.33 1536.2 716.32; 95933 263856 6.38 0.28 39.8 0.36 1632.6 765.24; 104790 285697 6.65 0.30 42.5 0.39 1753.2 812.22; 116694 308765 6.65 0.30 46.7 0.41 1865.5 875.26]'; t=[102569 52365 46251; 124587 60821 56245; 148792 69253 67362; 162568 79856 78165; 186592 91658 90548; 205862 99635 98758; 226598 109862 102564; 245636 120566 111257; 263595 130378 120356]'; a=[1 2 3 5 7 8]; P=p; for i=1:6 P(a(i),:)=(p(a(i),:)-min(p(a(i),:)))/(max(p(a(i),:))-min(p(a(i),:))); end for i=1:3 T(i,:)=(t(i,:)-min(t(i,:)))/(max(t(i,:))-min(t(i,:))); end P_train=[P(:,1) P(:,2) P(:,3) P(:,4) P(:,5) P(:,6) P(:,7)]; T_train=[T(:,1) T(:,2) T(:,3) T(:,4) T(:,5) T(:,6) T(:,7)]; P_test=[P(:,8) P(:,9)]; T_test=[T(:,8) T(:,9)]; for i=1:5 net=newgrnn(P_train,T_train,i/10); temp=sim(net,P_train); j=3*i; y_out(j-2,:)=temp(1,:); y_out(j-1,:)=temp(2,:); y_out(j,:)=temp(3,:); temp=sim(net,P_test); y(j-2,:)=temp(1,:); y(j-1,:)=temp(2,:); y(j,:)=temp(3,:); end y1=[y_out(1,:);y_out(2,:);y_out(3,:)]; y2=[y_out(4,:);y_out(5,:);y_out(6,:)]; y3=[y_out(7,:);y_out(8,:);y_out(9,:)]; y4=[y_out(10,:);y_out(11,:);y_out(12,:)]; y5=[y_out(13,:);y_out(14,:);y_out(15,:)]; y6=[y(1,:);y(2,:);y(3,:)]; y7=[y(4,:);y(5,:);y(6,:)]; y8=[y(7,:);y(8,:);y(9,:)]; y9=[y(10,:);y(11,:);y(12,:)]; y10=[y(13,:);y(14,:);y(15,:)]; for i=1:7 error1(i)=norm(y1(:,i)-T_train(:,i)); error2(i)=norm(y2(:,i)-T_train(:,i)); error3(i)=norm(y3(:,i)-T_train(:,i)); error4(i)=norm(y4(:,i)-T_train(:,i)); error5(i)=norm(y5(:,i)-T_train(:,i)); end for i=1:2 error6(i)=norm(y6(:,i)-T_test(:,i)); error7(i)=norm(y7(:,i)-T_test(:,i)); error8(i)=norm(y8(:,i)-T_test(:,i)); error9(i)=norm(y9(:,i)-T_test(:,i)); error10(i)=norm(y10(:,i)-T_test(:,i)); end axes(handles.axes1); title('网络的逼近误差'); plot(1:7,error1,'-*'); hold on; plot(1:7,error2,'-+'); hold on; plot(1:7,error3,'-h