Forecasting Model for Long Life Cycle of Complex Recycling Technical Systems by Improving the Structure of the Neural Network
Khalil A. Yaghi, Waheeb A. Abu-Dawwas
Journal of Networking Technology, Vol. 1, No. 4, 2010.
DOI: 
Link: http://www.dline.info/jnt/fulltext/v1n3/3.pdf
Abstract:
The purpose of this paper is to increase the efficiency of functionality and reliability of Complex Recycling Technical Systems (CRTS) community, by 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. The structure of the ACS life cycle of vehicles environment in the form of service “center” that service groups of “client”, exploiting the same type of objects, was developed.