Neural Network Toolbox

calca1

Calculate network signals for one time step

Syntax

[Ac,N,LWZ,IWZ,BZ] = calca1(net,Pd,Ai,Q)

Description

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

Calculating outputs for a single time step is useful for sequential iterative algorithms such as trains, which need to calculate the network response for each time step individually.

[Ac,N,LWZ,IWZ,BZ] = calca1(net,Pd,Ai,Q) takes,

•    net - Neural network.

     Pd - Delayed inputs for a single time step.

      Ai - Initial layer delay conditions for a single time step.

      Q - Concurrent size.

   and returns,

•     A - Layer outputs for the time step.

      N - Net inputs for the time step.

      LWZ - Weighted layer outputs for the time step.

      IWZ - Weighted inputs for the time step.

      BZ - Concurrent biases for the time step.

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)
  

calca

calce