DocumentCode :
1738109
Title :
Neural metrics-software metrics in artificial neural networks
Author :
Leung, W.K. ; Simpson, R.
Author_Institution :
Sch. of Comput., Birmingham Univ., UK
Volume :
1
fYear :
2000
fDate :
2000
Firstpage :
209
Abstract :
Backpropagation based supervised feedforward artificial neural networks (ANNs) have been developed for many applications (e.g. Rumelhart et al., 1986; Hinton, 1989; Werbos, 1990; and Riedmiller, 1994) but no detailed study of the measurement of the quality characteristics (e.g complexity and efficiency) of the network system has been made. Without an appropriate measurement, it is difficult to tell how the network performs on given applications. In addition, it is difficult to provide a measure of the algorithmic complexity of any given application. The paper proposes a new set of software metrics, named neural metrics, which provide indicative measures of the quality characteristics of ANNs. Neural metrics that are non-primitive in nature are defined mathematically as neural metrics functions
Keywords :
backpropagation; computational complexity; feedforward neural nets; software metrics; algorithmic complexity; backpropagation based supervised feedforward artificial neural networks; neural metrics; neural metrics functions; quality characteristics measurement; software metrics; Application software; Artificial neural networks; Backpropagation algorithms; Computer networks; Intelligent networks; Logic testing; Neural networks; Performance evaluation; Software metrics; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on
Conference_Location :
Brighton
Print_ISBN :
0-7803-6400-7
Type :
conf
DOI :
10.1109/KES.2000.885794
Filename :
885794
Link To Document :
بازگشت