【BP预测】基于BP神经网络实现数据预测含Matlab源码

function varargout = bp_demo(varargin) % BP_DEMO M-file for bp_demo.fig % BP_DEMO, by itself, creates a new BP_DEMO or raises the existing % singleton*. % % H = BP_DEMO returns the handle to a new BP_DEMO or the handle to % the existing singleton*. % % BP_DEMO( ’CALLBACK’ ,hObject,eventData,handles,...) calls the local % function named CALLBACK in BP_DEMO.M with the given input arguments. % % BP_DEMO( ’Property’ , ’Value’ ,...) creates a new BP_DEMO or raises the % existing singleton*. Starting from the left, property value pairs are % applied to the GUI before bp_demo_OpeningFcn gets called. An % unrecognized property name or invalid value makes property application % stop. All inputs are passed to bp_demo_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 bp_demo % Last Modified by GUIDE v2.5 19-May-2021 09:29:51 % Begin initialization code - DO NOT EDIT gui_Singleton = 1; gui_State = struct(’ gui_Name ’, mfilename, ... ’ gui_Singleton ’, gui_Singleton, ... ’ gui_OpeningFcn ’, @bp_demo_OpeningFcn, ... ’ gui_OutputFcn ’, @bp_demo_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 bp_demo is made visible. function bp_demo_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 bp_demo (see VARARGIN) % Choose default command line output for bp_demo handles.output = hObject; % Update handles structure guidata(hObject, handles); % UIWAIT makes bp_demo wait for user response (see UIRESUME) % uiwait(handles.figure1); % --- Outputs from this function are returned to the command line. function varargout = bp_demo_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 t_sim_Callback(hObject, eventdata, handles) % hObject handle to t_sim (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 t_sim as text % str2double(get(hObject,’ String ’)) returns contents of t_sim as a double % --- Executes during object creation, after setting all properties. function t_sim_CreateFcn(hObject, eventdata, handles) % hObject handle to t_sim (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 train_button. function train_button_Callback(hObject, eventdata, handles) % hObject handle to train_button (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) warning off global net P_train T_train N Epochs Show Goal Lr fcn_hidden fcn_output fcn_learn % default value N=10; Epochs=1000; Show=10; Goal=1e-3; Lr=0.01; fcn_hidden=’ tansig ’; fcn_output=’ purelin ’; fcn_learn=’ trainlm ’; % create neural network net=newff(minmax(P_train),[N size(T_train,1)],{fcn_hidden,fcn_output},fcn_learn); net.trainParam.epochs=Epochs; net.trainParam.show=Show; net.trainParam.goal=Goal; net.trainParam.lr=Lr; net.trainParam.showwindow=0; [net,tr]=train(net,P_train,T_train); % show waitbar perf=tr.perf; perf_t=abs(perf(2:end)-perf(1))/(perf(1)-perf(end)); h=waitbar(0,’ please wait....... ’); for i=1:length(perf_t) waitbar(perf_t(i)); end close(h); ‍