2016-02-052016-02-052012978-84-940056-6-4http://hdl.handle.net/11441/34155Spiking 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.application/pdfengAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Spiking neural P systemsFuzzy spiking neural P systemsAdaptive fuzzy spiking neural P systemsFuzzy reasoningLearning probleminfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccesshttps://idus.us.es/xmlui/handle/11441/34155