The used NN are listed in table 1.
|
_______________________Set 1_______________________ |
_____________________Set 2_____________________ | ||||
|
Topology |
Order |
Learning Law |
Topology |
Order |
Learning Law |
|
FF |
Bm |
FF |
Bm | ||
|
FF |
Bp |
FF |
Bp | ||
|
FF |
_____Sn_____ | ||||
|
Self |
DA |
Bp |
Self ~ |
DA |
Bp |
|
Self |
DA |
Bm | |||
|
Self |
SA |
Bm | |||
|
Self |
SA |
Bp | |||
|
Tasm |
DA |
Bm |
Tasm |
DA |
Bm |
|
Tasm |
DA |
Bp |
Tasm |
DA |
Bp |
|
Tasm |
SA |
Bm |
Tasm |
SA |
Bm |
|
Tasm |
SA |
Bp |
Tasm |
SA |
Bp |
|
Tasm |
SA |
Cm | |||
|
Tasm |
SA |
_____Sn_____ | |||
|
Learning Law: Bp = Back Propagation (standard) Sn = Sine Net (Semeion) Topology: FF = Feed Forward (standard) Self = Self Recurrent Network (Semeion) Order: DA = Dynamic and Adaptive Recurrency (Semeion) _____________________________SA = Static and Adaptive Recurrency (Semeion____________________ | |||||
Table 1 - The different architectures of SANN
7. Learning and validation of the SANNs
The Statistical functions used to evaluate the results are presented in Annexe 2. Each
function measures, separately, the error of each output vector component of SANNs
related to the correspondent Target value given in Input.
The first evaluation of the results is given by the statistical functions in table 2.
|
Residential |
Industrial |
Commercial |
Average | |
|
RMSE |
0.06756 |
0.05543 |
0.03290 |
0.09338 |
|
Real Error |
-0.00262 |
-0.00938 |
-0.00550 |
-0.00583 |
|
Relative Error |
0.05983 |
0.04911 |
0.01867 |
0.04254 |
|
Error Variance |
0.11412 |
0.09735 |
0.05214 |
0.08787 |
|
NMSE |
0.15737 |
0.22162 |
0.38873 |
0.25591 |
|
Squared R |
0.84310 |
0.78374 |
0.62216 |
0.74967 |
|
Linear Corr. |
0.91820 |
0.88529 |
0.78877 |
0.86409 |
Table 2 - Statistical measures of validation
12
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