Neural Network Toolbox

Input Weights

delays

This property defines a tapped delay line between the jth input and its weight to the ith layer.

• net.inputWeights{i,j}.delays
  

It must be set to a row vector of increasing 0 or positive integer values.

Side Effects..   Whenever this property is altered, the weight's size (net.inputWeights{i,j}.size) and the dimensions of its weight matrix (net.IW{i,j}) are updated.

initFcn

This property defines the function used to initialize the weight matrix going to the ith layer from the jth input, if the network initialization function is initlay, and the ith layer's initialization function is initwb.

• net.inputWeights{i,j}.initFcn
  

This function can be set to the name of any weight initialization function, including these toolbox functions.

Weight Initialization Functions
initzero	Zero-weight/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.

This function is used to calculate an initial weight matrix for the weight going to the ith layer from the jth input (net.IW{i,j}) when init is called, if the network initialization function (net.initFcn) is initlay, and the ith layer's initialization function (net.layers{i}.initFcn) is initwb.

• net = init(net)
  

Custom Functions..   See Advanced Topics for information on creating custom initialization functions.

learn

This property defines whether the weight matrix to the ith layer from the jth input is to be altered during training and adaption.

• net.inputWeights{i,j}.learn
  

It can be set to 0 or 1.

It enables or disables the weights learning during calls to either adapt or train.

• [net,Y,E,Pf,Af] = adapt(NET,P,T,Pi,Ai)
  [net,tr] = train(NET,P,T,Pi,Ai)
  

learnFcn

This property defines the function used to update the weight matrix going to the ith layer from the jth input during training, if the network training function is trainb, trainc, or trainr, or during adaption, if the network adapt function is trains.

• net.inputWeights{i,j}.learnFcn
  

It can be set to the name of any weight learning function, including these toolbox functions.

Weight Learning Functions
learngd	Gradient descent weight/bias learning function.
learngdm	Grad. 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.
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.

The learning function updates the weight matrix of the ith layer from the jth input (net.IW{i,j}) during calls to train, if the network training function (net.trainFcn) is trainb, trainc, or trainr, or during calls to adapt, if the network adapt function (net.adaptFcn) is trains.

• [net,Y,E,Pf,Af] = adapt(NET,P,T,Pi,Ai)
  [net,tr] = train(NET,P,T,Pi,Ai)
  

Custom Functions..   See Advanced Topics for information on creating custom learning functions.

learnParam

This property defines the learning parameters and values for the current learning function of the ith layer's weight coming from the jth input.

• net.inputWeights{i,j}.learnParam
  

The fields of this property depend on the current learning function (net.inputWeights{i,j}.learnFcn). Evaluate the above reference to see the fields of the current learning function.

Call help on the current learning function to get a description of what each field means.

• help(net.inputWeights{i,j}.learnFcn)
  

size (read-only)

This property defines the dimensions of the ith layer's weight matrix from the jth network input.

• net.inputWeights{i,j}.size
  

It is always set to a two-element row vector indicating the number of rows and columns of the associated weight matrix (net.IW{i,j}). The first element is equal to the size of the ith layer (net.layers{i}.size). The second element is equal to the product of the length of the weights delay vectors with the size of the jth input:

• length(net.inputWeights{i,j}.delays) * net.inputs{j}.size
  

userdata

This property provides a place for users to add custom information to the (i,j)th input weight.

• net.inputWeights{i,j}.userdata
  

Only one field is predefined. It contains a secret message to all Neural Network Toolbox users.

• net.inputWeights{i,j}.userdata.note
  

weightFcn

This property defines the function used to apply the ith layer's weight from the jth input to that input.

• net.inputWeights{i,j}.weightFcn
  

It can be set to the name of any weight function, including these toolbox functions.

Weight Functions
dist	Conscience bias initialization function.
dotprod	Zero-weight/bias initialization function.
mandist	Manhattan-distance weight function.
negdist	Normalized column-weight initialization function.
normprod	Normalized row-weight initialization function.

The weight function is used when sim is called to simulate the network.

• [Y,Pf,Af] = sim(net,P,Pi,Ai)
  

Custom functions..   See Advanced Topics for information on creating custom weight functions.

Biases

Layer Weights

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