Title :
A proposed life cycle forecasting model of complex recycling technical systems by implementing neural super network
Author :
Yaghi, Khalil A. ; Abu-Dawwas, Waheeb A.
Author_Institution :
Dept. of Manage. Inf. Syst., Appl. Sci. Private Univ., Jordan
Abstract :
The purpose of this paper is to increase the efficiency of functionality and reliability of complex recycling technical systems (CRTS) community, through improving the control quality of their life cycle. Automated control system (ACS) on the basis of neural super-network learning for forecasting damages and ensuring its information representation for learning was proposed. In the paper it was suggested an architecture and a method of learning of a neural super-network for forecasting the progress of CRTS community life cycle.
Keywords :
learning (artificial intelligence); neural nets; reliability theory; automated control system; complex recycling technical system; control quality; damage forecasting; functionality; information representation; life cycle forecasting model; neural super network learning; reliability; Artificial neural networks; Automatic control; Cathode ray tubes; Control systems; Information representation; Management information systems; Neural networks; Optical computing; Predictive models; Recycling; Automated Control System (ACS); Complex Recycling Technical Systems (CRTS); Neural Network (NN); Neural Super-Network;
Conference_Titel :
Networked Digital Technologies, 2009. NDT '09. First International Conference on
Conference_Location :
Ostrava
Print_ISBN :
978-1-4244-4614-8
Electronic_ISBN :
978-1-4244-4615-5
DOI :
10.1109/NDT.2009.5272222
