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.