Neural Network Toolbox

calcperf

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 networks 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 we create 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 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],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 we define the layer targets for the two neurons for each of the five time steps.

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

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

calcpd

cell2mat