Open AccessStellar Image Interpretation System Using Artificial Neural Networks:
Author(s) -
A. El-Bassuny Alawy,
Farag I. Y. Elnagahy,
Amran Haroon,
Yosry A. Azzam,
Boris Šimák
Publication year - 2004
Publication title -
acta polytechnica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.207
H-Index - 15
eISSN - 1805-2363
pISSN - 1210-2709
DOI - 10.14311/506
Subject(s) - artificial neural network , computer science , artificial intelligence , frame (networking) , set (abstract data type) , backpropagation , pattern recognition (psychology) , data set , function (biology) , polar , algorithm , physics , astronomy , telecommunications , evolutionary biology , biology , programming language
A supervised Artificial Neural Network (ANN) based system is being developed employing the Bi-polar function for identifying stellar images in CCD frames. It is based on feed-forward artificial neural networks with error back-propagation learning. It has been coded in C language. The learning process was performed on a 341 input pattern set, while a similar set was used for testing. The present approach has been applied on a CCD frame of the open star cluster M67. The results obtained have been discussed and compared with those derived in our previous work employing the Uni-polar function and by a package known in the astronomical community (DAOPHOT-II). Full agreement was found between the present approach, that of Elnagahy et al, and the standard astronomical data for the cluster. It has been shown that the developed technique resembles that of the Uni-Polar function, possessing a simple, much faster yet reliable approach. Moreover, neither prior knowledge on, nor initial data from, the frame to be analysed is required, as it is for DAOPHOT-II.