Adaptive fuzzy spiking neural P systems for fuzzy inference and learning |
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Authors: | Jun Wang |
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Affiliation: | School of Electrical and Information Engineering , Xihua University , Chengdu , Sichuan , 610039 , China |
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Abstract: | Fuzzy spiking neural P systems (in short, FSN P systems) are a novel class of distributed parallel computing models, which can model fuzzy production rules and apply their dynamic firing mechanism to achieve fuzzy reasoning. However, these systems lack adaptive/learning ability. Addressing this problem, a class of FSN P systems are proposed by introducing some new features, called adaptive fuzzy spiking neural P systems (in short, AFSN P systems). AFSN P systems not only can model weighted fuzzy production rules in fuzzy knowledge base but also can perform dynamically fuzzy reasoning. It is important to note that AFSN P systems have learning ability like neural networks. Based on neuron's firing mechanisms, a fuzzy reasoning algorithm and a learning algorithm are developed. Moreover, an example is included to illustrate the learning ability of AFSN P systems. |
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Keywords: | spiking neural P systems fuzzy spiking neural P systems adaptive fuzzy spiking neural P systems fuzzy reasoning learning problem |
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