@article {879,
	title = {Fuzzy reasoning spiking neural P system for fault diagnosis},
	journal = {Information Sciences},
	volume = {235},
	year = {2013},
	month = {06/2013},
	pages = {106{\textendash}116},
	publisher = {Elsevier},
	address = {Amsterdam (The Netherlands)},
	abstract = {Spiking neural P systems (SN P systems) have been well established as a novel class of distributed parallel computing models. Some features that SN P systems possess are attractive to fault diagnosis. However, handling fuzzy diagnosis knowledge and reasoning is required for many fault diagnosis applications. The lack of ability is a major problem of existing SN P systems when applying them to the fault diagnosis domain. Thus, we extend SN P systems by introducing some new ingredients (such as three types of neurons, fuzzy logic and new firing mechanism) and propose the fuzzy reasoning spiking neural P systems (FRSN P systems). The FRSN P systems are particularly suitable to model fuzzy production rules in a fuzzy diagnosis knowledge base and their reasoning process. Moreover, a parallel fuzzy reasoning algorithm based on FRSN P systems is developed according to neuron{\textquoteright}s dynamic firing mechanism. Besides, a practical example of transformer fault diagnosis is used to demonstrate the feasibility and effectiveness of the proposed FRSN P systems in fault diagnosis problem.},
	keywords = {Fault diagnosis; P systems; Spiking neural P systems; Fuzzy knowledge representation; Fuzzy reasoning},
	issn = {0020-0255},
	doi = {http://dx.doi.org/10.1016/j.ins.2012.07.015},
	url = {(http://dx.doi.org/10.1016/j.ins.2012.07.015)},
	author = {Hong Peng and Jun Wang and Mario J. P{\'e}rez-Jim{\'e}nez and Hao Wang and Jie Shao and Tao Wang}
}
@article {863,
	title = {Image Thresholding with Cell-like P Systems},
	journal = {Tenth Brainstorming Week on Membrane Computing},
	volume = {II},
	year = {2012},
	month = {02/2012},
	pages = {75-88},
	publisher = {F{\'e}nix Editora},
	address = {Seville, Spain},
	abstract = {P systems are a new class of distributed parallel computing models. In this
paper, a novel three-level thresholding approach for image segmentation based on celllike P systems is proposed in order to improve the computational efficiency of multilevel thresholding. A cell-like P system with a specially designed membrane structure is
developed and an improved evolution mechanism is integrated into the cell-like P system.
Due to parallel computing ability and particular mechanism of the cell-like P system, the
presented thresholding approach can effectively search the optimal thresholds for threelevel thresholding based on total fuzzy entropy. Experimental results of both qualitative
and quantitative comparisons for the proposed approach and GA-based and PSO-based
approaches illustrate the applicability and effectiveness.},
	keywords = {Celllike P systems, Image segmentation, Membrane computing, Thresholding approach, Total fuzzy entropy},
	url = {http://www.gcn.us.es/10BWMC/10BWMCvolII/papers/Hong\%20Peng\%20et\%20al\%20-\%2010th\%20BWMC.pdf},
	author = {Hong Peng and Jie Shao and Bing Li and Jun Wang and Mario J. P{\'e}rez-Jim{\'e}nez and Yang Jiang and Yufan Yang}
}
