In this paper we discuss the design of a cellular neural network (CNN) to solve a class of optimization problems of importance for communication networks. The CNN optimization capabilities are exploited to implement an efficient cell schedulingalgorithm in a fast packet switching fabric. The neural-based switching fabric maximizes the cell throughput and, at the same time, it is able to meet a variety of quality of service (QoS) requirements by optimizing a suitable function of the switchingdelay and priority of the cells. We also show that the CNN approach has advantages with respect to that based on Hopfield neural networks (HNN's) to solve the considered class of optimization problems. In particular, we exploit existing techniques todesign CNN's with a prescribed set of stable binary equilibrium points as a basic tool to suppress spurious responses and, hence to optimize the neural switching fabric performance.
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