ART-EMAP: A neural network architecture for object recognition by evidence accumulation

1995 ◽  
Vol 6 (4) ◽  
pp. 805-818 ◽  
Author(s):  
G.A. Carpenter ◽  
W.D. Ross
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Evidence Accumulation

Layered object recognition system using a hierarchical hybrid neural network architecture

1992 ◽  
Author(s):  
Srinivasan Raghavan ◽  
Naresh Gupta ◽  
Barbara A. Lambird ◽  
David Lavine ◽  
Laveen N. Kanall
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Network Architecture ◽  
Recognition System ◽  
Neural Network Architecture ◽  
Hybrid Neural Network

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

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