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Adaptive fuzzy spiking neural P systems for fuzzy inference and learning

Jun Wang, Hong Peng
2013 International Journal of Computer Mathematics  
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

Songhai Fan, Prithwineel Paul, Tianbao Wu, Haina Rong, Gexiang Zhang
2020 Applied Sciences  
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

Ye. Bodyanskiy, A. Dolotov
2010 Radìoelektronika, Ìnformatika, Upravlìnnâ  
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]

Derong Liu, Chin-Teng Lin, Garry Greenwood, Simon Lucas, Zhengyou Zhang
2013 IEEE Computational Intelligence Magazine  
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]

Artem Dolotov, Yevgeniy Bodyanskiy
2009 Image Processing  
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]

Nikola Kasabov
2013 Studies in Fuzziness and Soft Computing  
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

Yicen Liu, Ying Chen, Prithwineel Paul, Songhai Fan, Xiaomin Ma, Gexiang Zhang
2021 Applied Sciences  
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

Tao Wang, Gexiang Zhang, Mario J. Pérez-Jiménez
2015 International Journal of Computers Communications & Control  
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

Haina Rong, Mianjun Ge, Gexiang Zhang, Ming Zhu
2018 International Journal of Computers Communications & Control  
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

Nikola K. Kasabov
2015 Knowledge-Based Systems  
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

Jun Wang, J. Mario P´erez-Jim´enez, Hong Peng, Peng Shi, Min Tu
2016 Chinese journal of electronics  
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]

Akbal Rain, Mert Emre Saritac
2021 arXiv   pre-print
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

Mario J. Pérez-Jiménez, Carmen Graciani, David Orellana-Martín, Agustín Riscos-Núñez, Álvaro Romero-Jiménez, Luis Valencia-Cabrera
2017 Theoretical Computer Science  
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

Kengo ONODA, Haruhiko TAKASE, Hidehiko KITA, Hiroharu KAWANAKA
2019 Journal of Japan Society for Fuzzy Theory and Intelligent Informatics  
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

Akbal Rain, Mert Emre Saritac, Department of Electrical and Computer Engineering, Southern Illinois University of Carbondale, USA, Department of Computer Science, University of Massachusetts Dartmouth, USA
2021 Computational research progress in applied science and engineering  
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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