High order neural networks to control manufacturing systems - a comparison studyHigh order neural networks to control manufacturing systems - a comparison study Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2015-11-071998enIn this paper the neuro adaptive scheduling methodology is evaluated by comparing its performance with conventional schedulers, through simulation studies. The case study chosen constitutes an existing manufacturing cell, which can be viewed as a highly complex nonacyclic FMS, with extremely heterogenous part processing times. The results reveal superiority of our algorithm in terms of backlogging and inventory cost, system stability and work-in-process http://creativecommons.org/licenses/by/4.0/2736-273737th IEEE Conference on Decision and ControlProceedings of the 37th IEEE Conference on Decision and Control Rovithakis, George A., 1968- Gaganis Vasileios Γαγανης Βασιλειος Perrakis, Stelios Christodoulou Manolis Χριστοδουλου Μανωλης Institute of Electrical and Electronics Engineers Artificial neural networks Nets, Neural (Computer science) Networks, Neural (Computer science) Neural nets (Computer science) neural networks computer science artificial neural networks nets neural computer science networks neural computer science neural nets computer science