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Adaptive Fuzzy Spiking Neural P Systems for Fuzzy Inference and Learning

Opened Access Adaptive Fuzzy Spiking Neural P Systems for Fuzzy Inference and Learning
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Autor: Wang, Jun
Peng, Hong
Fecha: 2012
Publicado en: Proceedings of the Tenth Brainstorming Week on Membrane Computing, (2)235-248. Sevilla, E.T.S. de Ingeniería Informática, 30 de Enero-3 de Febrero, 2012,
ISBN/ISSN: 978-84-940056-6-4
Tipo de documento: Ponencia
Resumen: Spiking neural P systems (in short, SN P systems) and their variants, in- cluding fuzzy spiking neural P systems (in short, FSN P systems), generally lack learning ability so far. Aiming at this problem, a class of modi ed FSN P systems are proposed in this paper, called adaptive fuzzy spiking neural P systems (in short, AFSN P systems). The AFSN P systems not only can model weighted fuzzy production rules in fuzzy knowl- edge base but also can perform dynamically fuzzy reasoning. It is more important that the AFSN P systems have learning ability like neural networks. Based on neuron's ring mechanisms, a fuzzy reasoning algorithm and a learning algorithm are developed. An example is included to illustrate the learning ability of the AFSN P systems.
Tamaño: 204.9Kb
Formato: PDF

URI: http://hdl.handle.net/11441/34155

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