科研之友
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摘要
In order to extend capability of spiking neural P systems (SN P systems) to represent fuzzy knowledge and further to process fuzzy information, we propose an extended spiking neural P system in this paper, called fuzzy spiking neural P system (FSN P system). In the FSN P system, two types of neurons (fuzzy proposition neuron and fuzzy rule neuron), certain factor and new spiking rule are considered, and content of neuron is fuzzy number instead of natural number (the number of spikes) in SN P systems. Due to graphical nature and advantages of SN P systems, the FSN P system is especially suitable to model fuzzy production rules in a rule-based system. An example is used to illustrate fuzzy reasoning process based on the FSN P system. Due to distributed and parallel computing and dynamical firing characteristics of FSN P system, it can exhibit potential advantages on fuzzy reasoning. In addition, we compare the FSN P system with other methods on fuzzy knowledge representation and fuzzy reasoning.
