calca (Neural Network Toolbox)

Calculate network outputs and other signals

Syntax

[Ac,N,LWZ,IWZ,BZ] = calca(net,Pd,Ai,Q,TS)

Description

This function calculates the outputs of each layer in response to a network's delayed inputs and initial layer delay conditions.

[Ac,N,LWZ,IWZ,BZ] = calca(net,Pd,Ai,Q,TS) takes,

    net -- Neural network

    Pd -- Delayed inputs

    Ai -- Initial layer delay conditions

    Q -- Concurrent size

    TS -- Time steps

and returns,

   Ac -- Combined layer outputs = [Ai, calculated layer outputs]

   N -- Net inputs

   LWZ -- Weighted layer outputs

   IWZ -- Weighted inputs

   BZ -- Concurrent biases

Examples

Here we create a linear network with a single input element ranging from 0 to 1, three neurons, and a tap delay on the input with taps at zero, two, and four time steps. The network is also given a recurrent connection from layer 1 to itself with tap delays of [1 2].

net = newlin([0 1],3,[0 2 4]);
net.layerConnect(1,1) = 1;
net.layerWeights{1,1}.delays = [1 2];

Here is a single (Q = 1) input sequence P with eight time steps (TS = 8), and the four initial input delay conditions Pi, combined inputs Pc, and delayed inputs Pd.

P = {0 0.1 0.3 0.6 0.4 0.7 0.2 0.1};
Pi = {0.2 0.3 0.4 0.1};
Pc = [Pi P];
Pd = calcpd(net,8,1,Pc)

Here the two initial layer delay conditions for each of the three neurons are defined:

Ai = {[0.5; 0.1; 0.2] [0.6; 0.5; 0.2]};

Here we calculate the network's combined outputs Ac, and other signals described above.

[Ac,N,LWZ,IWZ,BZ] = calca(net,Pd,Ai,1,8)

boxdist calca1