Synthetic method back-propagation analytic hierarchy process (CROSBI ID 481030)
Prilog sa skupa u zborniku | sažetak izlaganja sa skupa | međunarodna recenzija
Podaci o odgovornosti
Kliček, Božidar ; Dobša, Jasminka ; Hunjak, Tihomir
engleski
Synthetic method back-propagation analytic hierarchy process
The article shows limitations of the method AHP which are the consequences of linear dependance of the output variable upon input variables. For this purpose we compare two ways of modelling profit function : using neural network and using AHP. Each of the quoted methods has some advantages: neural networks have a possibility of learning from data and modelling on linearity ; on the other hand, AHP method parameters are modelled by people - experts. The data base of cases solving the problem of supplying credits in banks is used to prove the limitations of AHP method. It is proved that AHP is the special case of Neural Network Back - Propagation with hierarhic levels where the identity is the transfer function . Further, it is showed that it is possible to reduce the hierarhical AHP network to the neural network with only one hidde level and the identity as the transfer function. The exactness of modelling the classical BP neural network is compared with the linear AHP network. Furthermore, it is showed that for the typical case of supplying credits AHP network is less exact than BP network. The inexactness of BP network is the result of noise i learning data and the imprecision of modelling the profit function ; on the other hand, the in exactness of AHP is the result of noise in data that experts give and on linear dependance of the profit functio upon data. To unite good properties of AHP a d BP method the example is given in which the Neural Network
AHP; neural networks; machine learning
nije evidentirano
nije evidentirano
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Podaci o prilogu
27-x.
1998.
objavljeno
Podaci o matičnoj publikaciji
Abstracts of KOI'98
Scitovski, Rudolf
Rovinj: Društvo za operacijska istraživanja
Podaci o skupu
7-th International Conference on Operational Research KOI'98
predavanje
30.09.1998-02.10.1998
Rovinj, Hrvatska