Design linear layer
net = newlind(P,T,Pi)
net = newlind(P,T,Pi)
takes these input
arguments,
P 

T 

Pi 

where each element Pi{i,k}
is an Ri
byQ
matrix,
and the default = []
; and returns a linear layer
designed to output T
(with minimum sum square error)
given input P
.
newlind(P,T,Pi)
can also solve for linear
networks with input delays and multiple inputs and layers by supplying
input and target data in cell array form:
P 
 Each element 
T 
 Each element 
Pi 
 Each element 
and returns a linear network with ID
input
delays, Ni
network inputs, and Nl
layers,
designed to output T
(with minimum sum square error)
given input P
.
You want a linear layer that outputs T
given P
for
the following definitions:
P = [1 2 3]; T = [2.0 4.1 5.9];
Use newlind
to design such a network and
check its response.
net = newlind(P,T); Y = sim(net,P)
You want another linear layer that outputs the sequence T
given
the sequence P
and two initial input delay states Pi
.
P = {1 2 1 3 3 2}; Pi = {1 3}; T = {5.0 6.1 4.0 6.0 6.9 8.0}; net = newlind(P,T,Pi); Y = sim(net,P,Pi)
You want a linear network with two outputs Y1
and Y2
that
generate sequences T1
and T2
,
given the sequences P1
and P2
,
with three initial input delay states Pi1
for input
1 and three initial delays states Pi2
for input
2.
P1 = {1 2 1 3 3 2}; Pi1 = {1 3 0}; P2 = {1 2 1 1 2 1}; Pi2 = {2 1 2}; T1 = {5.0 6.1 4.0 6.0 6.9 8.0}; T2 = {11.0 12.1 10.1 10.9 13.0 13.0}; net = newlind([P1; P2],[T1; T2],[Pi1; Pi2]); Y = sim(net,[P1; P2],[Pi1; Pi2]); Y1 = Y(1,:) Y2 = Y(2,:)
newlind
calculates weight W
and
bias B
values for a linear layer from inputs P
and
targets T
by solving this linear equation in the
least squares sense:
[W b] * [P; ones] = T