Neural Network Toolbox™

Calculate network outputs, signals, and performance

Syntax

• [perf,El,Ac,N,BZ,IWZ,LWZ]=calcperf(net,X,Pd,Tl,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.

[perf,El,Ac,N,LWZ,IWZ,BZ] = calcperf(net,X,Pd,Tl,Ai,Q,TS) takes

net	Neural network
X	Network weight and bias values in a single vector
Pd	Delayed inputs
Tl	Layer targets
Ai	Initial layer delay conditions
Q	Concurrent size
TS	Time steps

and returns

perf	Network performance
El	Layer errors
Ac	Combined layer outputs = [Ai, calculated layer outputs]
N	Net inputs
LWZ	Weighted layer outputs
IWZ	Weighted inputs
BZ	Concurrent biases

Examples

Here is a linear network with a single input element ranging from 0 to 1, two neurons, and a tap delay on the input with taps at 0, 2, and 4 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],2);
  net.layerConnect(1,1) = 1;
  net.layerWeights{1,1}.delays = [1 2];
  

Here is a single (Q = 1) input sequence P with five time steps (TS = 5), 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};
  Pi = {0.2 0.3 0.4 0.1};
  Pc = [Pi P];
  Pd = calcpd(net,5,1,Pc);
  

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

• Ai = {[0.5; 0.1] [0.6; 0.5]};
  

Here the layer targets for the two neurons for each of the five time steps are defined.

• Tl = {[0.1;0.2] [0.3;0.1], [0.5;0.6] [0.8;0.9], [0.5;0.1]};
  

Here the network's weight and bias values are extracted.

• X = getx(net);
  

Here the network's combined outputs Ac and other signals described above are calculated.

• [perf,El,Ac,N,BZ,IWZ,LWZ] = calcperf(net,X,Pd,Tl,Ai,1,5)
  

See Also

calcpd

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calcpd

combvec

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