An Approach for Detecting Fault Lines in a Small Current Grounding System using Fuzzy Reasoning Spiking Neural P Systems

This paper presents a novel approach for detecting fault lines in a small current grounding system using fuzzy reasoning spiking neural P systems. In this approach, six features of current/voltage signals in a small current grounding system are analyzed by considering transient and steady components, respectively; a fault measure is used to quantify the possibility that a line is faulty; information gain degree is discussed to weight the importance of each of the six features; rough set theory is applied to reduce the features; and finally a fuzzy reasoning spiking neural P system is used to construct fault line detection models. Six cases in a small current grounding system prove the effectiveness of the introduced approach.

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SPIKE TRAINS IN SPIKING NEURAL P SYSTEMS

International Journal of Foundations of Computer Science ◽

10.1142/s0129054106004212 ◽

2006 ◽

Vol 17 (04) ◽

pp. 975-1002 ◽

Cited By ~ 82

Author(s):

GHEORGHE PĂUN ◽

MARIO J. PÉREZ-JIMÉNEZ ◽

GRZEGORZ ROZENBERG

Keyword(s):

Spike Train ◽

Spike Trains ◽

P Systems ◽

P System ◽

Spiking Neural P System ◽

Spiking Neural P Systems ◽

Semilinear Sets ◽

Research Problems ◽

Electrical Impulses

We continue here the study of the recently introduced spiking neural P systems, which mimic the way that neurons communicate with each other by means of short electrical impulses, identical in shape (voltage), but emitted at precise moments of time. The sequence of moments when a neuron emits a spike is called the spike train (of this neuron); by designating one neuron as the output neuron of a spiking neural P system II, one obtains a spike train of II. Given a specific way of assigning sets of numbers to spike trains of II, we obtain sets of numbers computed by II. In this way, spiking neural P systems become number computing devices. We consider a number of ways to assign (code) sets of numbers to (by) spike trains, and prove then computational completeness: the computed sets of numbers are exactly Turing computable sets. When the number of spikes present in the system is bounded, a characterization of semilinear sets of numbers is obtained. A number of research problems is also formulated.

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

Complexity ◽

10.1155/2019/2635714 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16 ◽

Cited By ~ 10

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.

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Generation of cycle picture languages using sequential spiking neural P system

Advances in Mathematics: Scientific Journal ◽

10.37418/amsj.9.2.6 ◽

2020 ◽

pp. 583-590

Author(s):

C. Y. Preethi ◽

H. A. Christinal ◽

S. Jebasingh ◽

D. A. Chandy

Keyword(s):

Boolean Functions ◽

Theoretical Study ◽

P Systems ◽

P System ◽

Spiking Neural P System ◽

Spiking Neural P Systems ◽

Chain Code ◽

Computing Model ◽

Processing Information ◽

Picture Languages

Spiking Neural P Systems (SN P Systems) is a bio-inspired computing model, abstracting the model of brain in processing information using spikes and neurons. The theoretical study of the model has proved that it can compute sets of positive numbers, Boolean functions and string languages. Cycle picture language is a set of pictures obtained using cycle grammar and chain code representation. In this paper we aim to compute the cycle picture languages using a variant of SN P system namely, Sequential SN P System using neurons and spiking rules. We compute the cycle picture language of sequence of chains.

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ON STRING LANGUAGES GENERATED BY SPIKING NEURAL P SYSTEMS WITH ANTI-SPIKES

International Journal of Foundations of Computer Science ◽

10.1142/s0129054111007794 ◽

2011 ◽

Vol 22 (01) ◽

pp. 15-27 ◽

Cited By ~ 20

Author(s):

KAMALA KRITHIVASAN ◽

VENKATA PADMAVATI METTA ◽

DEEPAK GARG

Keyword(s):

P Systems ◽

P System ◽

Spiking Neural P System ◽

Spiking Neural P Systems ◽

Natural Way

An Spiking Neural P system with anti-spikes uses two types of objects called spikes and anti-spikes which can encode binary digits in a natural way. The step when system emits a spike or an anti-spike is associated with symbol 1 and 0, respectively. Here we consider these computing devices as language generators. They allow non-determinism between the rules ac → a and ac → ā, c ϵ ℕ, thus help to generate languages which cannot be generated using simple SN P systems.

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Simplified and Yet Turing Universal Spiking Neural P Systems with Communication on Request

International Journal of Neural Systems ◽

10.1142/s0129065718500132 ◽

2018 ◽

Vol 28 (08) ◽

pp. 1850013 ◽

Cited By ~ 46

Author(s):

Tingfang Wu ◽

Florin-Daniel Bîlbîe ◽

Andrei Păun ◽

Linqiang Pan ◽

Ferrante Neri

Keyword(s):

Neural Networks ◽

Membrane Computing ◽

P Systems ◽

P System ◽

Third Generation ◽

Turing Machines ◽

Spiking Neural P System ◽

Spiking Neural P Systems ◽

Turing Universality ◽

Theoretical Results

Spiking neural P systems are a class of third generation neural networks belonging to the framework of membrane computing. Spiking neural P systems with communication on request (SNQ P systems) are a type of spiking neural P system where the spikes are requested from neighboring neurons. SNQ P systems have previously been proved to be universal (computationally equivalent to Turing machines) when two types of spikes are considered. This paper studies a simplified version of SNQ P systems, i.e. SNQ P systems with one type of spike. It is proved that one type of spike is enough to guarantee the Turing universality of SNQ P systems. Theoretical results are shown in the cases of the SNQ P system used in both generating and accepting modes. Furthermore, the influence of the number of unbounded neurons (the number of spikes in a neuron is not bounded) on the computation power of SNQ P systems with one type of spike is investigated. It is found that SNQ P systems functioning as number generating devices with one type of spike and four unbounded neurons are Turing universal.

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Novel Numerical Spiking Neural P Systems with a Variable Consumption Strategy

Processes ◽

10.3390/pr9030549 ◽

2021 ◽

Vol 9 (3) ◽

pp. 549

Author(s):

Xiu Yin ◽

Xiyu Liu ◽

Minghe Sun ◽

Qianqian Ren

Keyword(s):

Production Functions ◽

P Systems ◽

P System ◽

Computing Device ◽

Spiking Neural P Systems ◽

Distributed Parallel Computing ◽

Production Values ◽

Novel Variant ◽

Turing Universality ◽

Variable Consumption

A novel variant of NSN P systems, called numerical spiking neural P systems with a variable consumption strategy (NSNVC P systems), is proposed. Like the spiking rules consuming spikes in spiking neural P systems, NSNVC P systems introduce a variable consumption strategy by modifying the form of the production functions used in NSN P systems. Similar to the delay feature of the spiking rules, NSNVC P systems introduce a postponement feature into the production functions. The execution of the production functions in NSNVC P systems is controlled by two, i.e., polarization and threshold, conditions. Multiple synaptic channels are used to transmit the charges and the production values in NSNVC P systems. The proposed NSNVC P systems are a type of distributed parallel computing models with a directed graphical structure. The Turing universality of the proposed NSNVC P systems is proved as number generating/accepting devices. Detailed descriptions are provided for NSNVC P systems as number generating/accepting devices. In addition, a universal NSNVC P system with 66 neurons is constructed as a function computing device.

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A Grid-Density Based Algorithm by Weighted Spiking Neural P Systems with Anti-Spikes and Astrocytes in Spatial Cluster Analysis

Processes ◽

10.3390/pr8091132 ◽

2020 ◽

Vol 8 (9) ◽

pp. 1132

Author(s):

Deting Kong ◽

Yuan Wang ◽

Xinyan Wu ◽

Xiyu Liu ◽

Jianhua Qu ◽

...

Keyword(s):

Dimensional Space ◽

P Systems ◽

High Dimensional ◽

P System ◽

Inhibitory Influence ◽

Spiking Neural P Systems ◽

Clustering Approach ◽

Spatial Cluster Analysis ◽

Effectiveness And Efficiency ◽

Real World Datasets

In this paper, we propose a novel clustering approach based on P systems and grid- density strategy. We present grid-density based approach for clustering high dimensional data, which first projects the data patterns on a two-dimensional space to overcome the curse of dimensionality problem. Then, through meshing the plane with grid lines and deleting sparse grids, clusters are found out. In particular, we present weighted spiking neural P systems with anti-spikes and astrocyte (WSNPA2 in short) to implement grid-density based approach in parallel. Each neuron in weighted SN P system contains a spike, which can be expressed by a computable real number. Spikes and anti-spikes are inspired by neurons communicating through excitatory and inhibitory impulses. Astrocytes have excitatory and inhibitory influence on synapses. Experimental results on multiple real-world datasets demonstrate the effectiveness and efficiency of our approach.

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Fuzzy reasoning spiking neural P systems revisited: A formalization

Theoretical Computer Science ◽

10.1016/j.tcs.2017.04.014 ◽

2017 ◽

Vol 701 ◽

pp. 216-225

Author(s):

Mario J. Pérez-Jiménez ◽

Carmen Graciani ◽

David Orellana-Martín ◽

Agustín Riscos-Núñez ◽

Álvaro Romero-Jiménez ◽

...

Keyword(s):

Fuzzy Reasoning ◽

P Systems ◽

Spiking Neural P Systems

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Performing Balanced Ternary Logic and Arithmetic Operations with Spiking Neural P Systems with Anti-Spikes

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.505.378 ◽

2012 ◽

Vol 505 ◽

pp. 378-385 ◽

Cited By ~ 2

Author(s):

Xian Wu Peng ◽

Xiao Ping Fan ◽

Jian Xun Liu

Keyword(s):

Logic Gates ◽

P Systems ◽

P System ◽

Ternary Logic ◽

Arithmetic Operations ◽

Spiking Neural P Systems ◽

Addition And Subtraction ◽

Distributed And Parallel Computing ◽

Computing Models ◽

Natural Way

Spiking neural P systems are a class of distributed and parallel computing models inspired by P systems and spiking neural networks.Spiking neural P system with anti-spikes can encode the balanced ternary three digits in a natural way using three states called anti-spikes, no-input and spikes. In this paper we use this variant of SN P system to simulate universal balanced ternary logic gates including AND,OR and NOT gate and to perform some basic balanced ternary arithmetic operations like addition and subtraction on balanced ternary integers. This paper provides an applicational answer to an open problem formulated by L.Pan and Gh. Păun.

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