A Log for Neural Networks
By: G. R.
Roosta
PART A1: EXISTING DATA TO TRAIN AND APPROVE
Primary (Basic/Classic) Learning Set
The
existing data (experience) is used to train an artificial neural network.
The number
of existing records (the number of existing input/output sets) is presented by
m.
From m
records, k records are used to train the artificial neural network and m-k
records are used to approve that the training would be successful.
Training
Set:
I1 → R1 OR
[i]1 → [r]1 (i.e., [existing input]1 →
[existing result]1)
I2 → R2 OR
[i]2 → [r]2
. . .
Ik → Rk OR
[i]k → [r]k
Approval Set:
Ik+1 → Rk+1 OR
[i]k+1 → [r]k+1
. . .
Im → Rm OR
[i]m → [r]m
PART B1: PRIMARY (BASIC/CLASSIC) LEARNING RULE
The bias
set is presented by B or [b].
The weight
set is presented by W or [w].
For each
training round, the output (actual result set) is presented by A or [a].
After q
training rounds:
1 ≤ q ≤ k
[b]q+1
= [b]q + ( [r]q -
[a]q )
[w]q+1
= [w]q + ( ( [r]q - [a]q ) × [i]q )
[continued
. . .]
SECTION Z: RESEARCH
PART Z1: GR’S NOVEL APPROACH – GA+NN
For each ([i]j
→ [r]j), from the population, select a team [t]j of best β
genes.
Form a population
Ps from [t]j ; 1 ≤ j ≤ m: Ps = Ū([t]j).
Since it is union and not union all:
|Ps|
≤ m × β.
For each [ps]a
from Ps, calculate Ëa:
Ëa
= S ëab ; 1 ≤ a ≤ |Ps|
& 1
≤ b ≤ m.
ëab
= 2 ×|Rb – Ab| / (|Rb| + | Ab|)
Select β genes
from Ps with the least Ë as Ts.
Each [ts]
like any NN/gene is:
[ts]j
= [bs]j + [ws]j .
Calculate
the initial biases and weights as:
B0
= S (1 / Ëj × [bs]j)
/ (S 1 / Ëj)
W0
= S (1 / Ëj × [ws]j)
/ (S 1 / Ëj)
See Contents
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