Yazar "Taskiran, Ugur" seçeneğine göre listele

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    Cost optimization of feed mixes by genetic algorithms
    (ELSEVIER SCI LTD, 2009) Sahman, M. Akif; Cunkas, Mehmet; Inal, Seref; Inal, Fatma; Coskun, Behic; Taskiran, Ugur
    The cost optimization is a key element to determine the least-cost feed mixture according to animals' nutrient requirements and the effective use of the sources. In this paper, the cost optimization of feeds is performed by genetic algorithm, considering the growing style and type, age, nutritional requirement and feedstuff costs for poultry and different types of animals. The proposed method is compared with linear programming approach to measure its performance. The obtained results show that Genetic algorithms could be applicable to the cost optimization of the feed mixtures. In addition, a software program is developed by using Delphi environment. which provides flexible, extensible and user-friendly framework for tuning the heuristic relevant parameters and improving the solution quality. (C) 2009 Elsevier Ltd. All rights reserved.
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    Neural Network Modelling of Breaking Force in Variable Road and Slope Conditions
    (WORLD ACAD UNION-WORLD ACAD PRESS, 2011) Kus, Recai; Taskiran, Ugur; Bayrakceken, Huseyin
    Most of the traffic accidents occur when the vehicle could not stop at desired point and time. The most important factors affecting the breaking performance are wheel and road conditions and parts which make up breaking system. Breaking test can be performed as road tests or can be carried out on breaking test equipment. Because road tests require special test areas and long time to complete, experiments performed with test equipment are preferred. The duration of tests and studies can be shortened by modeling experimental results with artificial neural networks (ANN). In this study, breaking outcomes are examined in different roads like linear and side slope roads, in different slipping conditions of the road; differences between the breaking performances are determined and mathematical model based on ANN is developed.