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Adaptive fuzzy spiking neural P systems for fuzzy inference and learning
2013
International Journal of Computer Mathematics
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Spiking neural P systems (in short, SN P systems) and their variants, including fuzzy spiking neural P systems (in short, FSN P systems), generally lack learning ability so far. ...
Aiming at this problem, a class of modified FSN P systems are proposed in this paper, called adaptive fuzzy spiking neural P systems (in short, AFSN P systems). ...
Conclusion In this paper, we presented a class of modified fuzzy spiking neural P systems: adaptive fuzzy spiking neural P systems (AFSN P systems, in short). ...
doi:10.1080/00207160.2012.743653
fatcat:z2pm4m5ieng4newc6abegrsiom
On Applications of Spiking Neural P Systems
2020
Applied Sciences
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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. ...
P Systems
rFRSNPS
Real Fuzzy Reasoning Spiking Neural P Systems
WFRSNPS
Weighted Fuzzy Reasoning Spiking Neural P Systems
MFRSNPS
Modified Fuzzy Reasoning Spiking Neural P Systems
TFSNPS
Time ...
doi:10.3390/app10207011
fatcat:fcclb7qetbhvfiiawet5alob7i
ANALOG-DIGITAL SELF-LEARNING FUZZY SPIKING NEURAL NETWORK AND ITS LEARNING ALGORITHM BASED ON 'WINNER-TAKES-MORE' RULE
2010
Radìoelektronika, Ìnformatika, Upravlìnnâ
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Analog-digital architecture of self-learning fuzzy spiking neural network is proposed in this paper. Spiking neuron synapse and some are treated in terms of classical automatic control theory. ...
Conventional unsupervised learning algorithm of spiking neural network is improved by applying 'Winner-Takes-More' rule. ...
FUZZY SPIKING NEURAL NETWORK AS ANALOG-DIGITAL SYSTEM Hardware implementations of spiking neural network demonstrated fast processing ability that made it possible to apply such systems in real-life applications ...
doi:10.15588/1607-3274-2010-1-20
fatcat:dg7mdd53ijbptekk7u3aum3why
CIS Publication Spotlight [Publication Spotlight]
2013
IEEE Computational Intelligence Magazine
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fuzzy spiking neural P systems (WFSN P sys- Digital Object Identifier: 10.1109/ tems). ...
In this paper the authors review existing methods and Weighted Fuzzy Spiking Neural P Sys- present new techniques to address this tems, by J. Wang, P. Shi, H. Peng, M. J. problem. ...
doi:10.1109/mci.2013.2264231
fatcat:adhxqtci7fc7plrtis62a2bsrm
Analog-Digital Self-Learning Fuzzy Spiking Neural Network in Image Processing Problems
[chapter]
2009
Image Processing
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Self-learning hybrid systems based on spiking neural network 6.1 Fuzzy receptive neurons A common peculiarity of artificial neural networks is that they store dependence of system model outputs on its ...
Spiking neural network as an analog-digital system
Introduction Hardware implementations of spiking neural network demonstrated fast processing ability that made it possible to apply such systems in ...
Analog-Digital Self-Learning Fuzzy Spiking Neural Network in Image Processing Problems, Image Processing, Yung-Sheng Chen (Ed.), ISBN: 978-953-307-026-1, InTech, Available from: http://www.intechopen.com ...
doi:10.5772/7061
fatcat:vy35kwljqzes7i7ejebssmt36m
The Evolution of the Evolving Neuro-Fuzzy Systems: From Expert Systems to Spiking-, Neurogenetic-, and Quantum Inspired
[chapter]
2013
Studies in Fuzziness and Soft Computing
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The review includes fuzzy expert systems, fuzzy neuronal networks, evolving connectionist systems, spiking neural networks, neurogenetic systems, and quantum inspired systems, all discussed from the point ...
This review is based on the author's personal (evolving) research, integrating principles from neural networks, fuzzy systems and nature. ...
Early work on the integration of neural networks and fuzzy systems for knowledge engineering: Neuro-fuzzy expert systems The seminal work by Lotfi Zadeh on fuzzy sets, fuzzy rules and intelligent systems ...
doi:10.1007/978-3-642-35641-4_41
fatcat:aghg3dtw3feyll2rzhhjljfanq
A Review of Power System Fault Diagnosis with Spiking Neural P Systems
2021
Applied Sciences
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Spiking neural P system (SNPS) is a popular parallel distributed computing model. It is inspired by the structure and functioning of spiking neurons. ...
It belongs to the category of neural-like P systems and is well-known as a branch of the third generation neural networks. ...
., WFRSNPS (weighted fuzzy reasoning spiking neural P system) was used to perform this task. ...
doi:10.3390/app11104376
fatcat:vekcwl7fzbh6fpgmyrhmoczkke
Fuzzy Membrane Computing: Theory and Applications
2015
International Journal of Computers Communications & Control
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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. ...
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. ...
spiking neural P systems with real numbers (AFRSN P systems), weighted fuzzy reasoning spiking neural P systems (WFRSN P systems) and fuzzy reasoning spiking neural P systems with trapezoidal fuzzy numbers ...
doi:10.15837/ijccc.2015.6.2080
fatcat:rqkmwjvh65ejxgwkcmznfasblm
An Approach for Detecting Fault Lines in a Small Current Grounding System using Fuzzy Reasoning Spiking Neural P Systems
2018
International Journal of Computers Communications & Control
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This paper presents a novel approach for detecting fault lines in a small current grounding system using fuzzy reasoning spiking neural P systems. ...
reasoning spiking neural P system is used to construct fault line detection models. ...
In general, there are two kinds of fuzzy reasoning spiking neural P systems (FRSN P systems) [36] : fuzzy reasoning spiking neural P system with real numbers (rFRSN P systems) [16] and fuzzy reasoning ...
doi:10.15837/ijccc.2018.4.3220
fatcat:uce5cvoenva4hdb7obf6jcl5r4
Evolving connectionist systems for adaptive learning and knowledge discovery: Trends and directions
2015
Knowledge-Based Systems
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More papers, data and software systems can be found at: http://www.kedri.aut.ac.nz, and: http://ncs.ethz/projects/evospike/. ...
Fuzzy neural networks and their further development as evolving connectionist systems are presented next in the paper.
Fuzzy neurons and fuzzy neural networks. ...
In the past 50 years several seminal works in the areas of neural networks (Amari, 1967; Amari, 1990) , fuzzy systems (Zadeh, 1965 ) and rule-based expert systems (Knowledge-Based Systems Journal, 1988 ...
doi:10.1016/j.knosys.2014.12.032
fatcat:hlnpj4saebaqjinyy6d4ioecnu
A Fault Diagnosis Method of Power Systems Based on an Improved Adaptive Fuzzy Spiking Neural P Systems and PSO Algorithms
2016
Chinese journal of electronics
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A new fault diagnosis method based on improved Adaptive fuzzy spiking neural P systems (in short, AFSN P systems) and Particle swarm optimization (PSO) algorithm is presented to improve the efficiency ...
Based on our previous works, this paper focuses on AFSN P systems inference algorithms and learning algorithms and builds the fault diagnosis model using improved AFSN P systems for diagnosing effectively ...
Furthermore, fuzzy reasoning spiking neural P system is applied in fault diagnosis in Ref. [12] . ...
doi:10.1049/cje.2016.03.019
fatcat:j5mc7hzrtna4tkeum254un2xse
HydroPower Plant Planning for Resilience Improvement of Power Systems using Fuzzy-Neural based Genetic Algorithm
[article]
2021
arXiv
pre-print
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Spiking Neural Network (SNN) used as the main deep learning techniques to optimizing this load frequency control which turns into Deep Spiking Neural Network (DSNN). ...
So, proposed controller means Fuzzy PD optimization with Genetic Algorithm will be used for LFC in small scale of hydropower system. ...
Also a new model proposed which can optimize Fuzzy-PD controller in LFC terms based Genetic Algorithm and then neural deep learning technique which used Deep Spiking Neural Network (DSNN) which can be ...
arXiv:2106.12042v1
fatcat:egae6uh6tbdvzk4z7n7khu7tfm
Fuzzy reasoning spiking neural P systems revisited: A formalization
2017
Theoretical Computer Science
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The underlying mechanism was conceived by bridging spiking neural P systems with fuzzy rule-based reasoning systems. ...
Keywords: Fuzzy knowledge representation Fuzzy reasoning Spiking neural P systems Fault diagnosis Research interest within membrane computing is becoming increasingly interdisciplinary. ...
More recently, a variant of SN P systems, the so-called Fuzzy Reasoning Spiking Neural P systems (FRSN P systems, for short) were defined in [2] , providing interesting features that make them suitable ...
doi:10.1016/j.tcs.2017.04.014
fatcat:3uqhtkl4pvgdbjj2nyrbi3taza
Response Function and Training Method to Improve Response to Successive Input Patterns in SpikeProp
2019
Journal of Japan Society for Fuzzy Theory and Intelligent Informatics
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SpikeProp 70% [1, 2] 2 [3] [4] [5-7] Bohte Booij SpikeProp [8, 9] AWD AWD: Adaptive Weight Decay [10] AWD ...
Kasabov: "SPAN:
Spike Pattern Association Neuron for Learning Spatio-Temporal
Spike Patterns," Int. J. of Neural Systems, Vol.22, No.4, 2012.
[7] B. Gardner and A. ...
Kasiński: "Supervised Learning in Spiking
Neural Networks with ReSuMe: Sequence Learning, Classifi-
cation, and Spike Shifting," Neural Computation, Vol.22, No.2,
pp. 467-510, 2010.
[6] A. ...
doi:10.3156/jsoft.31.1_613
fatcat:3qee7uqfu5bo3jlelqita5uhte
HydroPower Plant Planning for Resilience Improvement of Power Systems using Fuzzy-Neural based Genetic Algorithm
2021
Computational research progress in applied science and engineering
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Deep Spiking Neural Network (DSNN), Fuzzy Logic, Genetic Algorithm (GA), Hydropower, Load Frequency Control (LFC), Planning, Proportional-Derivative (PD). ...
Spiking Neural Network (SNN) used as the main deep learning techniques to optimizing this load frequency control which turns into Deep Spiking Neural Network (DSNN). ...
Also a new model proposed which can optimize Fuzzy-PD controller in LFC terms based Genetic Algorithm and then neural deep learning technique which used Deep Spiking Neural Network (DSNN) which can be ...
doi:10.52547/crpase.7.2.2351
fatcat:26wbaf2wdzhfnioyvqualaufli
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