scholarly journals Fuzzy Membrane Computing: Theory and Applications

2015 ◽  
Vol 10 (6) ◽  
pp. 144 ◽  
Author(s):  
Tao Wang ◽  
Gexiang Zhang ◽  
Mario J. Pérez-Jiménez
Keyword(s):  
Knowledge Representation ◽  
Membrane Computing ◽  
Fuzzy Theory ◽  
Research Direction ◽  
P Systems ◽  
Future Research ◽  
Knowledge Representation And Reasoning ◽  
Spiking Neural P Systems ◽  
Comprehensive Survey ◽  
Fuzzy Knowledge

<p>Fuzzy membrane computing is a newly developed and promising research direction in the area of membrane computing that aims at exploring the complex in- teraction between membrane computing and fuzzy theory. This paper provides a comprehensive survey of theoretical developments and various applications of fuzzy membrane computing, and sketches future research lines. The theoretical develop- ments are reviewed from the aspects of uncertainty processing in P systems, fuzzifica- tion of P systems and fuzzy knowledge representation and reasoning. The applications of fuzzy membrane computing are mainly focused on fuzzy knowledge representation and fault diagnosis. An overview of different types of fuzzy P systems, differences between spiking neural P systems and fuzzy reasoning spiking neural P systems and newly obtained results on these P systems are presented.</p>

SPIKING NEURAL P SYSTEMS: AN EARLY SURVEY

2007 ◽  
Vol 18 (03) ◽  
pp. 435-455 ◽  
Author(s):  
GHEORGHE PĂUN ◽  
MARIO J. PÉREZ-JIMÉNEZ ◽  
ARTO SALOMAA
Keyword(s):  
Membrane Computing ◽  
Normal Forms ◽  
P Systems ◽  
Research Topics ◽  
Open Problems ◽  
Computing Power ◽  
Spiking Neural P Systems ◽  
A Cell ◽  
Early Survey ◽  
Basic Ideas

Spiking neural P systems were introduced in the end of the year 2005, in the aim of incorporating in membrane computing the idea of working with unique objects ("spikes"), encoding the information in the time elapsed between consecutive spikes sent from a cell/neuron to another cell/neuron. More than one dozen of papers where written in the meantime, clarifying many of the basic properties of these devices, especially related to their computing power. The present paper quickly surveys the basic ideas and the basic results, presenting a complete to-date bibliography, and also giving a completing result related to the normal forms possible for spiking neural P systems: we prove that the indegree of such systems (the maximal number of incoming synapses of neurons) can be bounded by 2 without losing the computational completeness. A series of research topics and open problems are formulated.

scholarly journals On Applications of Spiking Neural P Systems

2020 ◽  
Vol 10 (20) ◽  
pp. 7011 ◽  
Author(s):  
Songhai Fan ◽  
Prithwineel Paul ◽  
Tianbao Wu ◽  
Haina Rong ◽  
Gexiang Zhang
Keyword(s):  
Power Systems ◽  
Membrane Computing ◽  
Fuzzy Reasoning ◽  
P Systems ◽  
Diverse Range ◽  
Spiking Neural P Systems ◽  
Advantages And Disadvantages ◽  
New Ideas

Over the years, spiking neural P systems (SNPS) have grown into a popular model in membrane computing because of their diverse range of applications. In this paper, we give a comprehensive summary of applications of SNPS and its variants, especially highlighting power systems fault diagnoses with fuzzy reasoning SNPS. We also study the structure and workings of these models, their comparisons along with their advantages and disadvantages. We also study the implementation of these models in hardware. Finally, we discuss some new ideas which can further expand the scope of applications of SNPS models as well as their implementations.

Spiking Neural P Systems

2011 ◽  
pp. 60-73 ◽  
Author(s):  
Gheorghe Paun ◽  
Mario J. Perez-Jimenez
Keyword(s):  
Computational Complexity ◽  
Membrane Computing ◽  
Research Area ◽  
P Systems ◽  
Neural Computing ◽  
Research Topics ◽  
Computing Power ◽  
Spiking Neural P Systems ◽  
Basic Ideas ◽  
Source Of Information

This chapter is a quick survey of spiking neural P systems, a branch of membrane computing which was recently introduced with motivation from neural computing based on spiking. Basic ideas, examples, some results (especially concerning the computing power and the computational complexity/efficiency), and several research topics are discussed. The presentation is succinct and informal, meant mainly to let the reader having a flavour of this research area. The additional references are an important source of information in this respect.

scholarly journals Automatic Implementation of Fuzzy Reasoning Spiking Neural P Systems for Diagnosing Faults in Complex Power Systems

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Haina Rong ◽  
Kang Yi ◽  
Gexiang Zhang ◽  
Jianping Dong ◽  
Prithwineel Paul ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power Systems ◽  
Large Scale ◽  
Membrane Computing ◽  
Good Description ◽  
Fuzzy Reasoning ◽  
P Systems ◽  
P System ◽  
Spiking Neural P Systems ◽  
Complex Power

As an important variant of membrane computing models, fuzzy reasoning spiking neural P systems (FRSN P systems) were introduced to build a link between P systems and fault diagnosis applications. An FRSN P system offers an intuitive illustration based on a strictly mathematical expression, a good fault-tolerant capacity, a good description for the relationships between protective devices and faults, and an understandable diagnosis model-building process. However, the implementation of FRSN P systems is still at a manual process, which is a time-consuming and hard labor work, especially impossible to perform on large-scale complex power systems. This manual process seriously limits the use of FRSN P systems to diagnose faults in large-scale complex power systems and has always been a challenging and ongoing task for many years. In this work we develop an automatic implementation method for automatically fulfilling the hard task, named membrane computing fault diagnosis (MCFD) method. This is a very significant attempt in the development of FRSN P systems and even of the membrane computing applications. MCFD is realized by automating input and output, and diagnosis processes consists of network topology analysis, suspicious fault component analysis, construction of FRSN P systems for suspicious fault components, and fuzzy inference. Also, the feasibility of the FRSN P system is verified on the IEEE14, IEEE 39, and IEEE 118 node systems.

Fuzzy Knowledge Representation and Reasoning of the Smart Grid Based on Medium Logic and its Application

2013 ◽  
Vol 347-350 ◽  
pp. 1888-1893 ◽  
Author(s):  
Jian Wang ◽  
Qiang Wang ◽  
Wei Qing Ma ◽  
Da Hai Yao
Keyword(s):  
Knowledge Representation ◽  
Smart Grid ◽  
Traditional Knowledge ◽  
Power Transmission ◽  
Threshold Value ◽  
Knowledge Representation And Reasoning ◽  
Knowledge Reasoning ◽  
Transmission And Distribution ◽  
Grid Based ◽  
Fuzzy Knowledge

Medium logic completely reflects both the contrary relation and the contradictory relation. This paper introduces fuzzy privative and opposition privative in traditional knowledge representation. It takes a power transmission and distribution program for example, adopts a ratio function of distance to define the fuzzy set's member functions. Moreover, one confirmed method is presented to define the value as well as the threshold value in fuzzy production rules.Finally it discusses fuzzy knowledge reasoning by a specific example.

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