<s>
An	O
artificial	B-Architecture
neural	I-Architecture
network	I-Architecture
's	O
learning	B-Algorithm
rule	I-Algorithm
or	O
learning	O
process	O
is	O
a	O
method	O
,	O
mathematical	O
logic	O
or	O
algorithm	O
which	O
improves	O
the	O
network	O
's	O
performance	O
and/or	O
training	O
time	O
.	O
</s>
<s>
A	O
learning	B-Algorithm
rule	I-Algorithm
may	O
accept	O
existing	O
conditions	O
(	O
weights	O
and	O
biases	O
)	O
of	O
the	O
network	O
and	O
will	O
compare	O
the	O
expected	O
result	O
and	O
actual	O
result	O
of	O
the	O
network	O
to	O
give	O
new	O
and	O
improved	O
values	O
for	O
weights	O
and	O
bias	O
.	O
</s>
<s>
Depending	O
on	O
the	O
complexity	O
of	O
actual	O
model	O
being	O
simulated	O
,	O
the	O
learning	B-Algorithm
rule	I-Algorithm
of	O
the	O
network	O
can	O
be	O
as	O
simple	O
as	O
an	O
XOR	O
gate	O
or	O
mean	B-Algorithm
squared	I-Algorithm
error	I-Algorithm
,	O
or	O
as	O
complex	O
as	O
the	O
result	O
of	O
a	O
system	O
of	O
differential	O
equations	O
.	O
</s>
<s>
The	O
learning	B-Algorithm
rule	I-Algorithm
is	O
one	O
of	O
the	O
factors	O
which	O
decides	O
how	O
fast	O
or	O
how	O
accurately	O
the	O
artificial	O
network	O
can	O
be	O
developed	O
.	O
</s>
<s>
It	O
is	O
to	O
be	O
noted	O
that	O
though	O
these	O
learning	B-Algorithm
rules	I-Algorithm
might	O
appear	O
to	O
be	O
based	O
on	O
similar	O
ideas	O
,	O
they	O
do	O
have	O
subtle	O
differences	O
,	O
as	O
they	O
are	O
a	O
generalisation	O
or	O
application	O
over	O
the	O
previous	O
rule	O
,	O
and	O
hence	O
it	O
makes	O
sense	O
to	O
study	O
them	O
separately	O
based	O
on	O
their	O
origins	O
and	O
intents	O
.	O
</s>
<s>
In	O
the	O
mid-1950s	O
it	O
was	O
also	O
applied	O
to	O
computer	O
simulations	O
of	O
neural	B-Architecture
networks	I-Architecture
.	O
</s>
<s>
Oja	B-Algorithm
's	I-Algorithm
Rule	I-Algorithm
,	O
BCM	O
Theory	O
are	O
other	O
learning	B-Algorithm
rules	I-Algorithm
built	O
on	O
top	O
of	O
or	O
alongside	O
Hebb	O
's	O
Rule	O
in	O
the	O
study	O
of	O
biological	O
neurons	O
.	O
</s>
<s>
The	O
perceptron	O
learning	B-Algorithm
rule	I-Algorithm
originates	O
from	O
the	O
Hebbian	O
assumption	O
,	O
and	O
was	O
used	O
by	O
Frank	O
Rosenblatt	O
in	O
his	O
perceptron	O
in	O
1958	O
.	O
</s>
<s>
Seppo	O
Linnainmaa	O
in	O
1970	O
is	O
said	O
to	O
have	O
developed	O
the	O
Backpropagation	B-Algorithm
Algorithm	O
but	O
the	O
origins	O
of	O
the	O
algorithm	O
go	O
back	O
to	O
the	O
1960s	O
with	O
many	O
contributors	O
.	O
</s>
<s>
It	O
is	O
a	O
generalisation	O
of	O
the	O
least	O
mean	O
squares	O
algorithm	O
in	O
the	O
linear	O
perceptron	O
and	O
the	O
Delta	O
Learning	B-Algorithm
Rule	I-Algorithm
.	O
</s>
<s>
Similar	O
to	O
the	O
perceptron	O
learning	B-Algorithm
rule	I-Algorithm
but	O
with	O
different	O
origin	O
.	O
</s>
<s>
It	O
was	O
developed	O
for	O
use	O
in	O
the	O
ADALAINE	B-Algorithm
network	O
,	O
which	O
differs	O
from	O
the	O
Perceptron	O
mainly	O
in	O
terms	O
of	O
the	O
training	O
.	O
</s>
<s>
This	O
makes	O
ADALINE	B-Algorithm
different	O
from	O
the	O
normal	O
perceptron	O
.	O
</s>
<s>
Delta	O
rule	O
(	O
DR	O
)	O
is	O
similar	O
to	O
the	O
Perceptron	O
Learning	B-Algorithm
Rule	I-Algorithm
(	O
PLR	O
)	O
,	O
with	O
some	O
differences	O
:	O
</s>
<s>
The	O
delta	O
rule	O
is	O
considered	O
to	O
a	O
special	O
case	O
of	O
the	O
back-propagation	B-Algorithm
algorithm	I-Algorithm
.	O
</s>
<s>
It	O
is	O
well	O
suited	O
to	O
finding	O
clusters	B-Algorithm
within	O
data	O
.	O
</s>
<s>
Models	O
and	O
algorithms	O
based	O
on	O
the	O
principle	O
of	O
competitive	O
learning	O
include	O
vector	B-Algorithm
quantization	I-Algorithm
and	O
self-organizing	B-Algorithm
maps	I-Algorithm
(	O
Kohonen	B-Algorithm
maps	I-Algorithm
)	O
.	O
</s>
