A proposed life cycle forecasting model of complex recycling technical systems by implementing neural super network
| Autor: | Khalil Yaghi, Waheeb A. Abu-Dawwas |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Reliability theory
Artificial neural network Automatic control business.industry Computer science Reliability (computer networking) Quality control Machine learning computer.software_genre Industrial engineering Management information systems CRTS Control system Artificial intelligence business computer |
| Zdroj: | 2009 First International Conference on Networked Digital Technologies. |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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