Abstract
This paper introduces the design of rough neurons based on rough sets. Rough neurons instantiate approximate reasoning in assessing knowledge gleaned from input data. Each neuron constructs upper and lower approximations as an aid to classifying inputs. The particular form of rough neuron considered in this paper relies on what is known as a rough membership function in assessing the accuracy of a classification of input signals. The architecture of a rough neuron includes one or more input ports which filter inputs relative to selected bands of values and one or more output ports which produce measurements of the degree of overlap between an approximation set and a reference set of values in classifying neural stimuli. A class of Petri nets called rough Petri nets with guarded transitions is used to model a rough neuron. An application of rough neural computing is briefly considered in classifying the waveforms of power system faults. The contribution of this article is the presentation of a Petri net model which can be used to simulate and analyze rough neural computations.
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Peters, J.F., Skowron, A., Suraj, Z., Han, L., Ramanna, S. (2000). Design of Rough Neurons: Rough Set Foundation and Petri Net Model. In: Raś, Z.W., Ohsuga, S. (eds) Foundations of Intelligent Systems. ISMIS 2000. Lecture Notes in Computer Science(), vol 1932. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39963-1_30
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DOI: https://doi.org/10.1007/3-540-39963-1_30
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