Pregled bibliografske jedinice broj: 68529
Synthetic method back propagation analytic hierarchy process
Synthetic method back propagation analytic hierarchy process // 7-th International Conference on Operational Research KOI'98
Rovinj, 1998. (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
Synthetic method back propagation analytic hierarchy process
Autori
Kliček, Božidar ; Dobša, Jasminka ; Hunjak, Tihomir
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
7-th International Conference on Operational Research KOI'98
/ - Rovinj, 1998
Skup
7-th International Conference on Operational Research KOI'98
Mjesto i datum
Rovinj, Hrvatska, 30.09.1998. - 02.10.1998
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
back propagation; AHP; analytic hierarchy process
Sažetak
The article shows limitatio s of the method AHP which are the
co seque ces of li ear depe da ce of the output variable upo i put variables.
For this purpose we compare two ways of modelli g profit fu ctio : usi g eural
etwork a d usi g AHP. Each of the quoted methods has some adva tages: eural
etworks have a possibility of lear i g from data a d modelli g o li earity; o
the other ha d, i AHP method parameters are modelled by people - experts. The
data base of cases solvi g the problem of supplyi g credits i ba ks is used to prove
the limitatio s of AHP method.
It is proved that AHP is the special case of Neural Network Back - Propagatio
with hierarhic levels where the ide tity is the tra sfer fu ctio . Further, it is showed
that it is possible to reduce the hierarhical AHP etwork to the eural etwork with
o ly o e hidde level a d the ide tity as the tra sfer fu ctio . The exact ess of
modelli g the classical BP eural etwork is compared with the li ear AHP etwork.
Furthermore, it is showed that for the typical case of supplyi g credits AHP etwork
is less exact tha BP etwork. The i exact ess of BP etwork is the result of oise
i lear i g data a d the imprecisio of modelli g the profit fu ctio ; o the other
ha d, the i exact ess of AHP is the result of oise i data that experts give a d
o li ear depe da ce of the profit fu ctio upo data. To u ite good properties of
AHP a d BP method the example is give i which the Neural Network BP lear s
through the data that we get as the result of applyi g the AHP method.
Izvorni jezik
Engleski
Znanstvena područja
Informacijske i komunikacijske znanosti
