Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5775716 | Applied Mathematics and Computation | 2017 | 7 Pages |
Abstract
We investigate the performance of two different small-world feedforward neural networks for the diagnosis of diabetes. We use the Pima Indians Diabetic Dataset as input. We have previously shown than the Watts-Strogatz small-world feedforward neural network delivers a better classification performance than conventional feedforward neural networks. Here, we compare this performance further with the one delivered by the Newman-Watts small-world feedforward neural network, and we show that the latter is better still. Moreover, we show that Newman-Watts small-world feedforward neural networks yield the highest output correlation as well as the best output error parameters.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Okan Erkaymaz, Mahmut Ozer, Matjaž Perc,