Ponencia
Adaptive Fuzzy Spiking Neural P Systems for Fuzzy Inference and Learning
Autor/es | Wang, Jun
Peng, Hong |
Fecha de publicación | 2012 |
Fecha de depósito | 2016-02-05 |
Publicado en |
|
ISBN/ISSN | 978-84-940056-6-4 |
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 ... 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. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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Jun Wang et al - 10th BWMC.pdf | 204.9Kb | [PDF] | Ver/ | |
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