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
More intriguing information
1. Estimating the Impact of Medication on Diabetics' Diet and Lifestyle Choices2. The name is absent
3. Infrastructure Investment in Network Industries: The Role of Incentive Regulation and Regulatory Independence
4. The name is absent
5. THE AUTONOMOUS SYSTEMS LABORATORY
6. Linkages between research, scholarship and teaching in universities in China
7. IMMIGRATION AND AGRICULTURAL LABOR POLICIES
8. The problem of anglophone squint
9. The mental map of Dutch entrepreneurs. Changes in the subjective rating of locations in the Netherlands, 1983-1993-2003
10. Who’s afraid of critical race theory in education? a reply to Mike Cole’s ‘The color-line and the class struggle’