% Neural Network Toolbox % Version 6.0.1 (R2008b Prerelease) 27-Jun-2008 % % Graphical user interface functions. % nctool - Neural network classification tool. % nftool - Neural network fitting tool. % nprtool - Neural Network pattern recognition tool. % nntool - Neural Network Toolbox graphical user interface. % nntraintool - Neural network training tool. % view - View a neural network. % % Analysis functions. % confusion - Classification confusion matrix. % errsurf - Error surface of single input neuron. % maxlinlr - Maximum learning rate for a linear layer. % roc - Receiver operating characteristic. % % Distance functions. % boxdist - Box distance function. % dist - Euclidean distance weight function. % mandist - Manhattan distance weight function. % linkdist - Link distance function. % % Formatting data. % combvec - Create all combinations of vectors. % con2seq - Convert concurrent vectors to sequential vectors. % concur - Create concurrent bias vectors. % dividevec - Create all combinations of vectors. % ind2vec - Convert indices to vectors. % minmax - Ranges of matrix rows. % nncopy - Copy matrix or cell array. % normc - Normalize columns of a matrix. % normr - Normalize rows of a matrix. % pnormc - Pseudo-normalize columns of a matrix. % quant - Discretize values as multiples of a quantity. % seq2con - Convert sequential vectors to concurrent vectors. % vec2ind - Convert vectors to indices. % % Initialize network functions. % initlay - Layer-by-layer network initialization function. % % Initialize layer functions. % initnw - Nguyen-Widrow layer initialization function. % initwb - By-weight-and-bias layer initialization function. % % Initialize weight and bias functions. % initcon - Conscience bias initialization function. % initzero - Zero weight/bias initialization function. % initsompc - Initialize SOM weights with principle components. % midpoint - Midpoint weight initialization function. % randnc - Normalized column weight initialization function. % randnr - Normalized row weight initialization function. % rands - Symmetric random weight/bias initialization function. % % Learning functions. % learncon - Conscience bias learning function. % learngd - Gradient descent weight/bias learning function. % learngdm - Gradient descent w/momentum weight/bias learning function. % learnh - Hebb weight learning function. % learnhd - Hebb with decay weight learning function. % learnis - Instar weight learning function. % learnk - Kohonen weight learning function. % learnlv1 - LVQ1 weight learning function. % learnlv2 - LVQ2 weight learning function. % learnos - Outstar weight learning function. % learnsomb - Batch self-organizing map weight learning function. % learnp - Perceptron weight/bias learning function. % learnpn - Normalized perceptron weight/bias learning function. % learnsom - Self-organizing map weight learning function. % learnwh - Widrow-Hoff weight/bias learning rule. % % Line search functions. % srchbac - Backtracking search. % srchbre - Brent's combination golden section/quadratic interpolation. % srchcha - Charalambous' cubic interpolation. % srchgol - Golden section search. % srchhyb - Hybrid bisection/cubic search. % % Net input functions. % netprod - Product net input function. % netsum - Sum net input function. % % Network creation functions. % network - Create a custom neural network. % newc - Create a competitive layer. % newcf - Create a cascade-forward backpropagation network. % newdtdnn - Create a distributed time delay neural network. % newelm - Create an Elman backpropagation network. % newfit - Createa a fitting network. % newff - Create a feed-forward backpropagation network. % newfftd - Create a feed-forward input-delay backprop network. % newgrnn - Design a generalized regression neural network. % newhop - Create a Hopfield recurrent network. % newlin - Create a linear layer. % newlind - Design a linear layer. % newlvq - Create a learning vector quantization network. % newnarx - Create a feed-forward backpropagation network with feedback % from output to input. % newnarxsp - Create an NARX network in series-parallel arrangement. % newp - Create a perceptron. % newpnn - Design a probabilistic neural network. % newpr - Create a pattern recognition network. % newrb - Design a radial basis network. % newrbe - Design an exact radial basis network. % newsom - Create a self-organizing map. % % Network transform functions. % sp2narx - Convert a series-parallel NARX network to parallel (feedback) form. % % Network update functions. % nnt2c - Update NNT 2.0 competitive layer. % nnt2elm - Update NNT 2.0 Elman backpropagation network. % nnt2ff - Update NNT 2.0 feed-forward network. % nnt2hop - Update NNT 2.0 Hopfield recurrent network. % nnt2lin - Update NNT 2.0 linear layer. % nnt2lvq - Update NNT 2.0 learning vector quantization network. % nnt2p - Update NNT 2.0 perceptron. % nnt2rb - Update NNT 2.0 radial basis network. % nnt2som - Update NNT 2.0 self-organizing map. % % Performance functions. % mae - Mean absolute error performance function. % mse - Mean squared error performance function. % msereg - Mean squared error with regularization performance function. % mseregec - Mean squared error with regularization and economization performance function. % sse - Sum squared error performance function. % % Plotting functions. % hintonw - Hinton graph of weight matrix. % hintonwb - Hinton graph of weight matrix and bias vector. % plotbr - Plot network performance for Bayesian regularization training. % plotconfusion - Plot classification confusion matrix. % plotep - Plot a weight-bias position on an error surface. % plotes - Plot an error surface of a single input neuron. % plotfit - Plot function fit. % plotpc - Plot classification line on perceptron vector plot. % plotperform - Plot network performance. % plotpv - Plot perceptron input/target vectors. % plotregression - Plot linear regression. % plotroc - plot receiver operating characteristic. % plotsom - Plot self-organizing map. % plotsomhits - Plot self-organizing map sample hits. % plotsomnc - Plot self-organizing map neighbor connections. % plotsomnd - Plot self-organizing map neighbor distances. % plotsompos - Plot self-organizing map weight positions. % plotsomtop - Plot self-organizing map topology. % plottrainstate - Plot training state values. % plotv - Plot vectors as lines from the origin. % plotvec - Plot vectors with different colors. % postreg - Post-training regression analysis. % % Processing data. % fixunknowns - Process matrix rows with unknown values. % mapminmax - Map matrix row minimum and maximum values to [-1 1]. % mapstd - Map matrix row means and deviations to standard values. % processpca - Processes matrix rows with principal component analysis. % removeconstantrows - Remove matrix rows with constant values. % removerows - Remove matrix rows with specified indices. % % Simulink support. % gensim - Generate a Simulink block to simulate a neural network. % % Topology functions. % gridtop - Grid layer topology function. % hextop - Hexagonal layer topology function. % randtop - Random layer topology function. % % Training functions. % trainb - Batch training with weight & bias learning rules. % trainbuwb - Batch unsupervised weights/bias learning. % trainbfg - BFGS quasi-Newton backpropagation. % trainbr - Bayesian regularization. % trainc - Cyclical order incremental training w/learning functions. % traincgb - Powell-Beale conjugate gradient backpropagation. % traincgf - Fletcher-Powell conjugate gradient backpropagation. % traincgp - Polak-Ribiere conjugate gradient backpropagation. % traingd - Gradient descent backpropagation. % traingdm - Gradient descent with momentum backpropagation. % traingda - Gradient descent with adaptive lr backpropagation. % traingdx - Gradient descent w/momentum & adaptive lr backpropagation. % trainlm - Levenberg-Marquardt backpropagation. % trainoss - One step secant backpropagation. % trainr - Random order incremental training w/learning functions. % trainrp - Resilient backpropagation (Rprop). % trains - Sequential order incremental training w/learning functions. % trainscg - Scaled conjugate gradient backpropagation. % % Transfer functions. % compet - Competitive transfer function. % hardlim - Hard limit transfer function. % hardlims - Symmetric hard limit transfer function. % logsig - Log sigmoid transfer function. % netinv - Inverse transfer function. % poslin - Positive linear transfer function. % purelin - Linear transfer function. % radbas - Radial basis transfer function. % satlin - Saturating linear transfer function. % satlins - Symmetric saturating linear transfer function. % softmax - Soft max transfer function. % tansig - Hyperbolic tangent sigmoid transfer function. % tribas - Triangular basis transfer function. % % Using networks. % sim - Simulate a neural network. % init - Initialize a neural network. % adapt - Allow a neural network to adapt. % train - Train a neural network. % disp - Display a neural network's properties. % display - Display the name and properties of a neural network variable. % % Weight functions. % convwf - Convolution weight function. % dist - Euclidean distance weight function. % dotprod - Dot product weight function. % mandist - Manhattan distance weight function. % negdist - Negative distance weight function. % normprod - Normalized dot product weight function. % scalprod - Scalar product weight function. % % Template custom functions. % template_distance - Template distance function. % template_init_layer - Template layer initialization function. % template_init_network - Template network initialization function. % template_init_wb - Template weight/bias initialization function. % template_learn - Template leaning function. % template_net_input - Template net input function. % template_new_network - Template new network function. % template_performance - Template performance function. % template_process - Template process function. % template_search - Template search function. % template_topology - Template topology function. % template_train - Template train function. % template_transfer - Template transfer function. % template_weight - Template weight function. % Copyright 1992-2008 The MathWorks, Inc.