matlab-基于前馈神经网络(FNN)和模糊逻辑网络(NF)的时间序列预测matlab对比仿真-源码

function [para,obj,hist] = TK_optimize(x,d,para0,mode,eta,max_epochs,varargin) %% Parse optional arguments p = inputParser; addParameter(p,'batch_size',1); addParameter(p,'shuffle',false); addParameter(p,'verbose',true); addParameter(p,'early_stop_epoch',false); addParameter(p,'N_early_stop',100); addParameter(p,'early_stop_tol',false); addParameter(p,'tol',1e-3); addParameter(p,'displaysec',0.1); parse(p,varargin{:}); % Extract optional arguments as variables if used often batch_size = p.Results.batch_size; %% Perform some sanity cheks [N, ~] = size(x); % Number of training examples if batch_size > N error('Batch size = %d should be lower than the total amount of training examples = %d',batch_size,N); end if ~ ismember(mode,[0,1]) error('mode should be either 0 for FNN or 1 for TS-ANFIS'); end %% Initialise optimization hist = nan(1,max_epochs + 1); obj_i = SSE(x,d,para0,mode); %Inital objective value hist(1) = obj_i; para_i = para0; % Parameters that are updated in the loop obj = obj_i; % Lower training error (updated in loop) para = para_i; % The parameters with lowest training error (updated in loop) progress_count = 0; % Count for how many epochs the training error does not fall % Compute how many batches the training data is split into N_batches = floor(N/batch_size); % The last batch might have to be bigger last_batch_size = batch_size + N - batch_size*N_batches; batch_sizes = [repmat(batch_size,1,N_batches - 1), last_batch_size]; lineLength = 0; % Used to print progress time0 = clock; time0 = time0(6); %% Run SQD for a specified number of epochs for i = 1:max_epochs % Shuffle training data if prompted - stochastic gradient descent if p.Results.shuffle perm = randperm(N); else perm = 1:N; end x_perm = x(perm,:); d_perm = d(perm,:); %% Run epoch for j = 0:(N_batches - 1) % loop over each minibatch %% Unpack parameters and initialise gradients % Initialise gradients with the first training example in minibatch x1 = x(j*batch_size+1,:); d1 = d(j*batch_size+1,:); switch mode case 0 % FNN [W,b,h_modes] = para_i{:}; % Get correct size gradients by 1 call of backpropagation [g_W, g_b] = TK_FNN_grad(x1,d1,W,b,h_modes); M = length(g_W); % Number of layers case 1 % TS-ANFIS [c,sigma,theta] = para_i{:}; % Get correct size gradients by 1 call of backpropagation [g_c, g_sigma,g_theta] = TK_NF_grad(x1,d1,c,sigma,theta); end %% Run minibatches for k = 2:batch_sizes(j+1) % loop over each training example in minibatch idx = j*batch_size + k; % Current training example xk = x_perm(idx,:); dK = d_perm(idx,:); switch mode % Get sub gradients for training example case 0 % FNN [g_W_k,g_b_k] = TK_FNN_grad(xk,dK,W,b,h_modes); % add subgradiens to total gradient for l = 1:M g_W{l} = g_W{l} + g_W_k{l}; g_b{l} = g_b{l} + g_b_k{l}; end case 1 % TS-ANFIS [g_c_k, g_sigma_k,g_theta_k] = TK_NF_grad(... xk,dK,c,sigma,theta); % add subgradiens to total gradient g_c = g_c + g_c_k; g_sigma = g_sigma + g_sigma_k; g_theta = g_theta + g_theta_k; end end switch mode case 0 % FNN for l = 1:M W{l} = W{l} - eta*g_W{l}./batch_size; b{l} = b{l} - eta*g_b{l}./batch_size; end case 1 % TS-ANFIS % Update via SQD c = c - eta*g_c; sigma = sigma - eta*g_sigma./batch_size; theta = theta - eta*g_theta./batch_size; end % Finally pack parameters again switch mode case 0 % FNN para_i = {W,b,h_modes}; case 1 % TS_ANFIS para_i = {c,sigma,theta}; end end %% Record objective value obj_i = SSE(x,d,para_i,mode); hist(i + 1) = obj_i; % Update the final output parameter if lower SSE is achieved if obj_i < obj obj = obj_i; para = para_i; progress_count = 0; elseif p.Results.early_stop_epoch progress_count = progress_count + 1; if progress_count == p.Results.N_early_stop fprintf('Training error has not been lowered for %d epochs. Stopping training!\n',progress_count); break; end end if p.Results.early_stop_tol && (obj <= p.Results.tol) fprintf('Desired tolerence met with SSE = %.2e. Stopping training!\n',obj); break; end if p.Results.verbose time1 = clock; time1 = time1(6); % Get seconds if time1 - time0 > p.Results.displaysec || i == max_epochs time0 = time1; % Set up progressbar progress_percent = i/max_epochs; tmp = round(40*progress_percent); if i < max_epochs arrow = '>'; else arrow = '='; end progressbar = sprintf('|%s%s%s|',repmat('=',1,tmp-1),... arrow,repmat(' ',1,40-tmp)); fprintf(repmat('\b',1,lineLength)); % Clear line % Print all lineLength = fprintf('Progress: %.0f%% %s Epoch: %d, SSE = %.2e',... progress_percent*100,progressbar,i,obj); end end end if p.Results.verbose fprintf('\n'); end end