fuzzy-nnsfam.rar_fuzzy NN matlab_nn fuzzy_sfam_模糊神经网络

function net = train(data, labels, net, debug) % TRAIN Trains the given SFAM network on the given labeled data. % TNET = TRAIN(DATA, LABELS, NET, DEBUG) traines the given SFAM network NET on DATA with % labels LABELS. DATA is M-by-D matrix where M is the number of samples and D is the size of % the feature space. LABELS is a M-vector containing the labels for each data. NET can be an % untrained or previously trained SFAM network. % DEBUG is a scalar to control the verbosity of the program during training. If 0, nothing will % be printed, otherwise every DEBUG iterations an informatory line will be printed. % % TNET is the trained NET. % % Emre Akbas, May 2006 % %dbstop in train at 18 for e=1:net.epochs network_changed = false; tic; for s=1:size(data,1) if mod(s,debug)==0 elapsed = toc; fprintf(1,'Training on %dth sample, in %dth epoch.\5# of prototypes=%4d\tElapsed seconds: %f\n',s,e,length(net.weights),elapsed); tic; end input = data(s,:); input_label = labels(s); % Complement code input input = [input 1-input]; % Set vigilance ro = net.vigilance; % By default, create_new_prototype=true. Only if 'I' resonates with % one of the existing prototypes, a new prot. will not be created create_new_prototype = true; % Compute the activation values for each prototype. activation = ones(1,length(net.weights)); for i=1:length(net.weights) activation(i) = sum(min(input,net.weights{i}))/... (net.alpha + sum(net.weights{i})); end % Sort activation values [sortedActivations, sortedIndices] = sort(activation,'descend'); % Iterate over the prototypes with decreasing activation-value for p=sortedIndices % Compute match of the current candidate prototype match = sum(min(input,net.weights{p}))/net.D; % see note [1] % Check resonance if match>=ro % Check labels if input_label==net.labels(p) % update the prototype net.weights{p} = net.beta*(min(input,net.weights{p})) + ... (1-net.beta)*net.weights{p}; network_changed = true; create_new_prototype = false; break; else % Match-tracking begins. Increase vigilance ro = sum(min(input,net.weights{p}))/net.D + net.epsilon; end end end if create_new_prototype new_index = length(net.weights)+1; if net.singlePrecision net.weights{new_index} = ones(1,2*net.D,'single'); else net.weights{new_index} = ones(1,2*net.D); end net.weights{new_index} = net.beta*(min(input,net.weights{new_index})) + ... (1-net.beta)*net.weights{new_index}; net.labels(new_index) = input_label; network_changed = true; end end % samples loop if ~network_changed fprintf(1,'Network trained in %d epochs.\n',e); break end end % epochs loop % % NOTES: % ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ % [1] 'net.D' is written insyead of |input|. These values are equal. %