Developing Neuro-Fuzzy Logic Controllers for Flexible Manufacturing Systems

小行星9400146 这项研究将开发一个标准的方法来创建有效的柔性制造系统（FMS）控制器，使用模糊逻辑（FL）和神经网络（NN）技术的组合。 这种组合被称为神经模糊逻辑控制器（NFLC），并在自动化制造系统的控制中有希望。 为了促进NFLC的开发，将使用广义随机Petri网（GSPN）。 具体来说，FMS的GSPN将被划分为两个部分-一个用于物理工厂，一个用于控制器。 FMS的生产率在很大程度上取决于所采用的控制器算法的有效性和效率。 目前的FMS控制器在这些措施方面远远不够。

9400146 Choobineh This research will develop a standard methodology for creating effective Flexible Manufacturing System (FMS) controllers, using a combination of Fuzzy Logic (FL) and Neural Network (NN) technologies. This combination is known as a Neuro-Fuzzy Logic Controller (NFLC), and holds promise in the control of automated manufacturing systems. To facilitate the development of an NFLC, Generalized Stochastic Petri Nets (GSPNs) will be used. Specifically the GSPN of an FMS will be partitioned into two subnets - one for the physical plant, and one for the controller. The productivity of an FMS is highly dependent upon the effectiveness and efficiency of the controller algorithms employed. Current FMS controllers fall substantially short on these measures.