In: Burkhard, H.-D.; Czaja, L.; Skowron, A.; Starke, P.: Report No. 140: Proceedings of the workhop on Concurrency, Specification and Programming, Oct 9-11, 2000, pages 203-211. Berlin: Humboldt-University, 2000.
Abstract: This paper considers models of sensors and filters with Petri nets defined in the context of rough sets. Sensors are fundamental computational units in the design of decision systems and neural computing systems. The intent of this work is to construct Petri nets to simulate conditional computation in neural computing systems, which are dependent on filtered input from sensors. In particular, there is interest in bringing to light the computational features of the beginning layer ow what are known as rough membership function neural networks. In this paper, coloured Petri nets underlie the definition of a family of Petri nets based on rough set theory. Sensors are modeled relative to what are known as receptor processes in rough Petri nets. Filters are modeled as Lukasiewicz guards on some transitions in rough Petri nets. A Luksiewicz guard is defined in the context of multivalued logic. The contribution of this paper is the modeling of sensors and filters in the context of receptor processes and Lukasiewicz guards, respectively.
Keywords: approximation, enabling, filter, guard, multivalued logic, neuron, Petri net, rough sets,sensor..