Browsing by Author "Ziarati, M."
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Item Metadata only Application of neural networks in logistic systems.(2002) Ziarati, M.; Stockton, David; Uçan, Osman N.; Bilgili, E.Item Metadata only Design and development of a factory of the future in Turkey.(IEEE, 2002) Ziarati, R.; Ziarati, M.; Uçan, Osman N.; Stockton, DavidThe Factory of the Future programme of work embraces a number of collaborative research projects primarily concerned with factory automation. The current research encompasses the development of a laser device for machine tool calibration and a wireless network for application in manufacturing factories. A further work concerns research into design of a knowledge-based-system (KBS) for information automation as the basis for automating an entire manufacturing enterprise. The latter work is hoped to lead to the introduction of Intelligent Integrated Product Cycle (I2PC). This approach involves the development of a self-learning automated management system, which can be applied in a manufacturing enterprise, large or small. The paper makes special references to the I2 PC approach and the proposed neural network system and their application in automation of information and production processes within an enterprise.Item Metadata only Design and development of material and information flow for supply chaıns using genetic cellular networks.(Dogus University, 2002) Ziarati, M.; Stockton, David; Bilgili, E.; Uçan, Osman N.In a recent paper by authors (Ziarati and Ucan, January 2001) a Back Propagation-Artificial Neural Network (BP-ANN) was adapted for predicting the required car parts quantities in a real and major auto parts supplier chain. It was argued that due to the learning ability of neural networks, their speed and capacity to handle large amount of data, they have a potential for predicting components requirements and establishing associated scheduling throughout a given supply chain system.This paper should be considered a continuation of the first paper as the neural network approach introduced in this paper replaces the BP-ANN by a new method viz., Genetic Cellular Neural Network (GCNN). The latter approach requires by far less stability parameters and hence better suited to fast changing scenarios as in real supply chain applications.The model has shown promising outcomes in learning and predicting material demand in a supply chain, with high degree of accuracy.Item Metadata only Design and development of ships using an expert system applying a novel multi-layered neural networks.(Istanbul Technical University, 2009) Urkmez, S.; Ziarati, R.; Bilgili, E.; Ziarati, M.; Stockton, DavidItem Metadata only Developing a mechanism for learning in engineering environments.(2002) Ziarati, M.; Stockton, David; Uçan, Osman N.Item Metadata only Genetic cellular neural network applications for prediction purposes in industry.(Istanbul University, 2003) Ziarati, M.; Stockton, David; Bilgili, E.; Uçan, Osman N.Item Metadata only The next generation of enterprise resource planning (ERP) Systems incorporating a neural network forecasting tool.(IEEE, 2003) Ziarati, M.; Stockton, David; Bilgili, E.; Uçan, Osman N.