Neural Network Toolbox

Utility Function Variables

These variables are used only by the utility functions.

Pc

Combined inputs.

Ni-by-(ID+TS) cell array, each element P{i,ts} is an Ri-by-Q matrix.

Pc = [Pi P] = Initial input delay conditions and network inputs.

Pd

Delayed inputs.

Ni-by-Nj-by-TS cell array, each element Pd{i,j,ts} is an (Ri*IWD(i,j))-by-Q matrix, where IWD(i,j) is the number of delay taps associated with input weight to layer i from input j.

Equivalently, IWD(i,j) = length(net.inputWeights{i,j}.delays).

Pd is the result of passing the elements of P through each input weights tap delay lines. Since inputs are always transformed by input delays in the same way it saves time to only do that operation once, instead of for every training step.

BZ

Concurrent bias vectors.

Nl-by-1 cell array, each element BZ{i} is a Si-by-Q matrix.

Each matrix is simply Q copies of the net.b{i} bias vector.

IWZ

Weighted inputs.

Ni-by-Nl-by-TS cell array, each element IWZ{i,j,ts} is a Si-by--by-Q matrix.

LWZ

Weighed layer outputs.

Ni-by-Nl-by-TS cell array, each element LWZ{i,j,ts} is a Si-by-Q matrix.

N

Net inputs.

Ni-by-TS cell array, each element N{i,ts} is a Si-by-Q matrix.

A

Layer outputs.

Nl-by-TS cell array, each element A{i,ts} is a Si-by-Q matrix.

Ac

Combined layer outputs.

Nl-by-(LD+TS) cell array, each element A{i,ts} is a Si-by-Q matrix.

Ac = [Ai A] = Initial layer delay conditions and layer outputs.

Tl

Layer targets.

Nl-by-TS cell array, each element Tl{i,ts} is a Si-by-Q matrix.

Tl contains empty matrices [] in rows of layers i not associated with targets, indicated by net.targets(i) == 0.

El

Layer errors.

Nl-by-TS cell array, each element El{i,ts} is a Si-by-Q matrix.

El contains empty matrices [] in rows of layers i not associated with targets, indicated by net.targets(i) == 0.

X

Column vector of all weight and bias values.

Variables

Functions

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