| Neural Network Toolbox | |
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Function Reference | Alphabetical List |
| Analysis Functions |
Analyze network properties |
| Distance Functions |
Compute distance between two vectors |
| Graphical Interface Functions |
Open GUIs for building neural networks |
| Layer Initialization Functions |
Initialize layer weights |
| Learning Functions |
Learning algorithms used to adapt networks |
| Line Search Functions |
Line-search algorithms |
| Net Input Functions |
Sum excitations of layer |
| Network Initialization Function |
Initialize network weights |
| New Networks Functions |
Create network architectures |
| Network Use Functions |
High-level functions to manipulate networks |
| Performance Functions |
Measure network performance |
| Plotting Functions |
Plot and analyze networks and network performance |
| Processing Functions |
Preprocess and postprocess data |
| Simulink® Support Function |
Generate Simulink® block for network simulation |
| Topology Functions |
Arrange neurons of layer according to specific topology |
| Training Functions |
Train networks |
| Transfer Functions |
Transform output of network layer |
| Utility Functions |
Internal utility functions |
| Vector Functions |
Internal functions for network computations |
| Weight and Bias Initialization Functions |
Initialize weights and biases |
| Weight Functions |
Convolution, dot product, scalar product, and distances weight functions |
Analysis Functions
| errsurf |
Error surface of single-input neuron |
| confusion |
Classification confusion matrix |
| maxlinlr |
Maximum learning rate for linear neuron |
| roc |
Receiver operating characteristic |
Distance Functions
| boxdist |
Distance between two position vectors |
| dist |
Euclidean distance weight function |
| linkdist |
Link distance function |
| mandist |
Manhattan distance weight function |
Graphical Interface Functions
| nctool |
Neural network classification tool |
| nftool |
Open Neural Network Fitting Tool |
| nntool |
Open Network/Data Manager |
| nntraintool |
Neural network training tool |
| nprtool |
Neural network pattern recognition tool |
| view |
View a neural network |
Layer Initialization Functions
| initnw |
Nguyen-Widrow layer initialization function |
| initwb |
By-weight-and-bias layer initialization function |
Learning Functions
| learncon |
Conscience bias learning function |
| learngd |
Gradient descent weight/bias learning function |
| learngdm |
Gradient descent with momentum weight/bias learning function |
| learnh |
Hebb weight learning function |
| learnhd |
Hebb with decay weight learning rule |
| 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 |
| learnp |
Perceptron weight and bias learning function |
| learnpn |
Normalized perceptron weight and bias learning function |
| learnsom |
Self-organizing map weight learning function |
| learnsomb |
Batch self-organizing map weight learning function |
| learnwh |
Widrow-Hoff weight and bias learning rule |
Line Search Functions
| srchbac |
1-D minimization using backtracking search |
| srchbre |
1-D interval location using Brent's method |
| srchcha |
1-D minimization using Charalambous' method |
| srchgol |
1-D minimization using golden section search |
| srchhyb |
1-D minimization using hybrid bisection/cubic search |
Net Input Functions
| netprod |
Product net input function |
| netsum |
Sum net input function |
Network Initialization Function
| initlay |
Layer-by-layer network initialization function |
Network Use Functions
| adapt |
Allow neural network to change weights and biases on inputs |
| disp |
Neural network's properties |
| display |
Name and properties of neural network's variables |
| init |
Initialize neural network |
| sim |
Simulate neural network |
| train |
Train neural network |
New Networks Functions
| network |
Create custom neural network |
| newc |
Create competitive layer |
| newcf |
Create cascade-forward backpropagation network |
| newdtdnn |
Create distributed time delay neural network |
| newelm |
Create Elman backpropagation network |
| newff |
Create feedforward backpropagation network |
| newfftd |
Create feedforward input-delay backpropagation network |
| newfit |
Create a fitting network |
| newgrnn |
Design generalized regression neural network |
| newhop |
Create Hopfield recurrent network |
| newlin |
Create linear layer |
| newlind |
Design linear layer |
| newlrn |
Create layered-recurrent network |
| newlvq |
Create learning vector quantization network |
| newnarx |
Create feedforward backpropagation network with feedback from output to input |
| newnarxsp |
Create NARX network in series-parallel arrangement |
| newp |
Create perceptron |
| newpnn |
Design probabilistic neural network |
| newpr |
Create a pattern recognition network |
| newrb |
Design radial basis network |
| newrbe |
Design exact radial basis network |
| newsom |
Create self-organizing map |
| sp2narx |
Convert series-parallel NARX network to parallel (feedback) form |
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 weight and bias position on error surface |
| plotes |
Plot error surface of single-input neuron |
| plotfit |
Plot function fit |
| plotpc |
Plot classification line on perceptron vector plot |
| plotperf |
Plot network performance |
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 origin |
| plotvec |
Plot vectors with different colors |
| postreg |
Postprocess trained network response with linear regression |
Processing Functions
| fixunknowns |
Process data by marking rows with unknown values |
| mapminmax |
Process matrices by mapping row minimum and maximum values to [-1 1] |
| mapstd |
Process matrices by mapping each row's means to 0 and deviations to 1 |
| processpca |
Process columns of matrix with principal component analysis |
| removeconstantrows |
Process matrices by removing rows with constant values |
| removerows |
Process matrices by removing rows with specified indices |
Simulink® Support Function
| gensim |
Generate Simulink® block for neural network simulation |
Topology Functions
| gridtop |
Gridtop layer topology function |
| hextop |
Hexagonal layer topology function |
| randtop |
Random layer topology function |
Training Functions
| trainb |
Batch training with weight and bias learning rules |
| trainbfg |
BFGS quasi-Newton backpropagation |
| trainbfgc |
BFGS quasi-Newton backpropagation for use with NN model reference adaptive controller |
| trainbr |
Bayesian regularization |
| trainbuwb |
Batch unsupervised weight/bias training |
| trainc |
Cyclical order incremental update |
| traincgb |
Powell-Beale conjugate gradient backpropagation |
| traincgf |
Fletcher-Powell conjugate gradient backpropagation |
| traincgp |
Polak-Ribiére conjugate gradient backpropagation |
| traingd |
Gradient descent backpropagation |
| traingda |
Gradient descent with adaptive learning rule backpropagation |
| traingdm |
Gradient descent with momentum backpropagation |
| traingdx |
Gradient descent with momentum and adaptive learning rule backpropagation |
| trainlm |
Levenberg-Marquardt backpropagation |
| trainoss |
One step secant backpropagation |
| trainr |
Random order incremental training with learning functions |
| trainrp |
Resilient backpropagation (Rprop) |
| trains |
Sequential order incremental training with 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 |
Softmax transfer function |
tansig |
Hyperbolic tangent sigmoid transfer function |
tribas |
Triangular basis transfer function |
Utility Functions
| calcgx |
Calculate weight and bias performance gradient as single vector |
| calcjejj |
Calculate Jacobian performance vector |
| calcjx |
Calculate weight and bias performance Jacobian as single matrix |
| calcpd |
Calculate delayed network inputs |
| calcperf |
Calculate network outputs, signals, and performance |
| getx |
All network weight and bias values as single vector |
| setx |
Set all network weight and bias values with single vector |
Vector Functions
| combvec |
Create all combinations of vectors |
| con2seq |
Convert concurrent vectors to sequential vectors |
| concur |
Create concurrent bias vectors |
| ind2vec |
Convert indices to vectors |
| minmax |
Ranges of matrix rows |
| normc |
Normalize columns of matrix |
| normr |
Normalize rows of matrix |
| pnormc |
Pseudonormalize columns of matrix |
| quant |
Discretize values as multiples of quantity |
| seq2con |
Convert sequential vectors to concurrent vectors |
| vec2ind |
Convert vectors to indices |
Weight and Bias Initialization Functions
| initcon |
Conscience bias initialization function |
| initsompc |
Initialize SOM weights with principal components |
| initzero |
Zero weight and bias initialization function |
| midpoint |
Midpoint weight initialization function |
| randnc |
Normalized column weight initialization function |
| randnr |
Normalized row weight initialization function |
| rands |
Symmetric random weight/bias initialization function |
| revert |
Change network weights and biases to previous initialization values |
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 |
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