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Journal articles on the topic 'Automata networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 November 2024

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1

NEHANIV, CHRYSTOPHER L. "ASYNCHRONOUS AUTOMATA NETWORKS CAN EMULATE ANY SYNCHRONOUS AUTOMATA NETWORK." International Journal of Algebra and Computation 14, no. 05n06 (2004): 719–39. http://dx.doi.org/10.1142/s0218196704002043.

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We show that any locally finite automata network [Formula: see text] with global synchronous updates can be emulated by another one [Formula: see text], whose structure derives from that of [Formula: see text] by a simple construction, but whose updates are made asynchronously at its various component automata (e.g. possibly randomly or sequentially, with or without possible simultaneous updates at different nodes). By "emulation", we refer to the existence of a spatial-temporal covering 'local time', allowing one to project the behavior of [Formula: see text] continuously onto that of [Formul

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Costa, Felipe Xavier, Jordan C. Rozum, Austin M. Marcus, and Luis M. Rocha. "Effective Connectivity and Bias Entropy Improve Prediction of Dynamical Regime in Automata Networks." Entropy 25, no. 2 (2023): 374. http://dx.doi.org/10.3390/e25020374.

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Biomolecular network dynamics are thought to operate near the critical boundary between ordered and disordered regimes, where large perturbations to a small set of elements neither die out nor spread on average. A biomolecular automaton (e.g., gene, protein) typically has high regulatory redundancy, where small subsets of regulators determine activation via collective canalization. Previous work has shown that effective connectivity, a measure of collective canalization, leads to improved dynamical regime prediction for homogeneous automata networks. We expand this by (i) studying random Boole

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Torgashev, Valery Antony. "Dynamic Automata Networks." SPIIRAS Proceedings 4, no. 27 (2014): 23. http://dx.doi.org/10.15622/sp.27.2.

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Bridoux, Florian, Maximilien Gadouleau, and Guillaume Theyssier. "Expansive automata networks." Theoretical Computer Science 843 (December 2020): 25–44. http://dx.doi.org/10.1016/j.tcs.2020.06.019.

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5

BOCCARA, NINO. "RANDOMIZED CELLULAR AUTOMATA." International Journal of Modern Physics C 18, no. 08 (2007): 1303–12. http://dx.doi.org/10.1142/s0129183107011339.

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We define and study a few properties of a class of random automata networks. While regular finite one-dimensional cellular automata are defined on periodic lattices, these automata networks, called randomized cellular automata, are defined on random directed graphs with constant out-degrees and evolve according to cellular automaton rules. For some families of rules, a few typical a priori unexpected results are presented.

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Afridi, Muhammad Ishaq. "Cognition in a Cognitive Routing System for Mobile Ad-Hoc Network through Leaning Automata and Neural Network." Applied Mechanics and Materials 421 (September 2013): 694–700. http://dx.doi.org/10.4028/www.scientific.net/amm.421.694.

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A cognitive routing system intelligently selects one protocol at a time for specific routing conditions and environment in MANET. Cognition or self-learning can be achieved in a cognitive routing system for mobile ad-hoc network (MANET) through a learning system like learning automata or neural networks. This article covers the application of learning automata and neural network to achieve cognition in MANET routing system. Mobile Ad-hoc networks are dynamic in nature and lack any fixed infrastructure, so the implementation of cognition enhances the performance of overall routing system in the

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GU, G. Q. "STATISTICAL PROPERTIES OF THE ADDITIVE AUTOMATON NETWORKS." International Journal of Modern Physics B 18, no. 17n19 (2004): 2463–68. http://dx.doi.org/10.1142/s0217979204025518.

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A set of theorems about the evolutionary properties of the automaton networks are presented in this article, which includes the theorems that, for an additive automaton network, the tree structures are identical, and that the geometric entropy equals to its topological entropy. The exact analytical formula for the entropy of the additive automaton networks is introduced, and applied to investigate the evolutionary properties of the elementary cellular automata of number 90 and number 150.

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Anderson, Taylor, and Suzana Dragićević. "Representing Complex Evolving Spatial Networks: Geographic Network Automata." ISPRS International Journal of Geo-Information 9, no. 4 (2020): 270. http://dx.doi.org/10.3390/ijgi9040270.

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Many real-world spatial systems can be conceptualized as networks. In these conceptualizations, nodes and links represent system components and their interactions, respectively. Traditional network analysis applies graph theory measures to static network datasets. However, recent interest lies in the representation and analysis of evolving networks. Existing network automata approaches simulate evolving network structures, but do not consider the representation of evolving networks embedded in geographic space nor integrating actual geospatial data. Therefore, the objective of this study is to

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Khomami, Mohammad Mehdi Daliri, Alireza Rezvanian, and Mohammad Reza Meybodi. "Distributed learning automata-based algorithm for community detection in complex networks." International Journal of Modern Physics B 30, no. 08 (2016): 1650042. http://dx.doi.org/10.1142/s0217979216500429.

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Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton

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10

Goles, E., and G. Y. Vichniac. "Invariants in automata networks." Journal of Physics A: Mathematical and General 19, no. 16 (1986): L961—L965. http://dx.doi.org/10.1088/0305-4470/19/16/003.

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11

SMITH, DAVID M. D., JUKKA-PEKKA ONNELA, CHIU FAN LEE, MARK D. FRICKER, and NEIL F. JOHNSON. "NETWORK AUTOMATA: COUPLING STRUCTURE AND FUNCTION IN DYNAMIC NETWORKS." Advances in Complex Systems 14, no. 03 (2011): 317–39. http://dx.doi.org/10.1142/s0219525911003050.

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We introduce Network Automata, a framework which couples the topological evolution of a network to its structure. To demonstrate its implementation we describe a simple model which exhibits behavior similar to the "Game of Life" before recasting some simple, well-known network models as Network Automata. We then introduce Functional Network Automata which are useful for dealing with networks in which the topology evolves according to some specified microscopic rules and, simultaneously, there is a dynamic process taking place on the network that both depends on its structure but is also capabl

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Neidig, Jörg, and Jan Lunze. "DECENTRALISED DIAGNOSIS OF AUTOMATA NETWORKS." IFAC Proceedings Volumes 38, no. 1 (2005): 400–405. http://dx.doi.org/10.3182/20050703-6-cz-1902.01869.

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Neidig, Jörg, and Jan Lunze. "DIRECT FEEDBACK IN AUTOMATA NETWORKS." IFAC Proceedings Volumes 38, no. 1 (2005): 19–24. http://dx.doi.org/10.3182/20050703-6-cz-1902.00288.

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14

Bridoux, Florian, Alonso Castillo-Ramirez, and Maximilien Gadouleau. "Complete simulation of automata networks." Journal of Computer and System Sciences 109 (May 2020): 1–21. http://dx.doi.org/10.1016/j.jcss.2019.12.001.

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15

Goles Ch, E. "Dynamics of positive automata networks." Theoretical Computer Science 41 (1985): 19–32. http://dx.doi.org/10.1016/0304-3975(85)90057-x.

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Jakobi, Sebastian, Katja Meckel, Carlo Mereghetti, and Beatrice Palano. "The descriptional power of queue automata of constant length." Acta Informatica 58, no. 4 (2021): 335–56. http://dx.doi.org/10.1007/s00236-021-00398-7.

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AbstractWe consider the notion of a constant length queue automaton—i.e., a traditional queue automaton with a built-in constant limit on the length of its queue—as a formalism for representing regular languages. We show that the descriptional power of constant length queue automata greatly outperforms that of traditional finite state automata, of constant height pushdown automata, and of straight line programs for regular expressions, by providing optimal exponential and double-exponential size gaps. Moreover, we prove that constant height pushdown automata can be simulated by constant length

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17

Manolios, Peter, and Robert Fanelli. "First-Order Recurrent Neural Networks and Deterministic Finite State Automata." Neural Computation 6, no. 6 (1994): 1155–73. http://dx.doi.org/10.1162/neco.1994.6.6.1155.

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We examine the correspondence between first-order recurrent neural networks and deterministic finite state automata. We begin with the problem of inducing deterministic finite state automata from finite training sets, that include both positive and negative examples, an NP-hard problem (Angluin and Smith 1983). We use a neural network architecture with two recurrent layers, which we argue can approximate any discrete-time, time-invariant dynamic system, with computation of the full gradient during learning. The networks are trained to classify strings as belonging or not belonging to the gramm

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Dimitrichenko, Dmitriy Petrovich. "Optimization of a recurrent neural network using automata with a variable structure." Программные системы и вычислительные методы, no. 4 (April 2023): 30–43. http://dx.doi.org/10.7256/2454-0714.2023.4.69011.

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The subject of this study is to identify a set of common structural properties inherent in recurrent neural networks and stochastic automata, the feature of which is purposeful behavior in dynamic environments. At the same time, the necessary commonality of properties is revealed both in the process of their functioning and in the process of their training (tuning). The author considers in detail such topics as: formalization of purposeful behavior, consideration of the design of automata, as well as a comparative analysis of the considered designs of automata. From the revealed commonality of

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19

Nikishin, K. I. "Method of Complex Control of Traffic Transmission in Software Defined Networks." LETI Transactions on Electrical Engineering & Computer Science 16, no. 5 (2023): 49–58. http://dx.doi.org/10.32603/2071-8985-2023-16-5-49-58.

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Modern requirements for traffic processing should possess the properties of mobility, speed, and simplification of network equipment administration. However, the existing classical Ethernet networks with Quality of Service failed to meet these requirements, which led to the emergence of software defined networks (SDN). In SDN, the main protocol is OpenFlow, which enables processing and managing heterogeneous traffic. This article aims to propose a methodology for eliminating the disadvantages inherent in the OpenFlow protocol. A method for complex control of traffic transmission in SDN was dev

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20

Cleeremans, Axel, David Servan-Schreiber, and James L. McClelland. "Finite State Automata and Simple Recurrent Networks." Neural Computation 1, no. 3 (1989): 372–81. http://dx.doi.org/10.1162/neco.1989.1.3.372.

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We explore a network architecture introduced by Elman (1988) for predicting successive elements of a sequence. The network uses the pattern of activation over a set of hidden units from time-step t−1, together with element t, to predict element t + 1. When the network is trained with strings from a particular finite-state grammar, it can learn to be a perfect finite-state recognizer for the grammar. When the network has a minimal number of hidden units, patterns on the hidden units come to correspond to the nodes of the grammar, although this correspondence is not necessary for the network to

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21

FOWDUR, S. C., and S. D. D. V. RUGHOOPUTH. "AUTOMATED TRAFFIC ROUTING BASED ON THE RIP AND EIGRP ALGORITHMS USING CELLULAR AUTOMATA." International Journal of Modern Physics C 21, no. 02 (2010): 239–47. http://dx.doi.org/10.1142/s0129183110015087.

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The Routing Information Protocol (RIP) and Enhanced Interior Gateway Routing Protocol (EIGRP) algorithms are widely used in the field of telecommunications for transmission of data over computer networks. Traffic Cellular Automata (TCA) is a technique that has proved to be very efficient in simulating large-scale road traffic networks. In this paper a multicell TCA model that includes anticipation and probability randomization has been hybridized with the RIP and EIGRP algorithms. Simulation was performed on a small network and the performance of these two algorithms compared and analyzed. The

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22

Burgin, Mark. "Triadic Automata and Machines as Information Transformers." Information 11, no. 2 (2020): 102. http://dx.doi.org/10.3390/info11020102.

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Algorithms and abstract automata (abstract machines) are used to describe, model, explore and improve computers, cell phones, computer networks, such as the Internet, and processes in them. Traditional models of information processing systems—abstract automata—are aimed at performing transformations of data. These transformations are performed by their hardware (abstract devices) and controlled by their software (programs)—both of which stay unchanged during the whole computational process. However, in physical computers, their software is also changing by special tools such as interpreters, c

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23

Xu, Zhiwu, Cheng Wen, Shengchao Qin, and Mengda He. "Extracting automata from neural networks using active learning." PeerJ Computer Science 7 (April 19, 2021): e436. http://dx.doi.org/10.7717/peerj-cs.436.

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Deep learning is one of the most advanced forms of machine learning. Most modern deep learning models are based on an artificial neural network, and benchmarking studies reveal that neural networks have produced results comparable to and in some cases superior to human experts. However, the generated neural networks are typically regarded as incomprehensible black-box models, which not only limits their applications, but also hinders testing and verifying. In this paper, we present an active learning framework to extract automata from neural network classifiers, which can help users to underst

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24

Kokash, Natallia, Mohammad Mahdi Jaghoori, and Farhad Arbab. "From Timed Reo Networks to Networks of Timed Automata." Electronic Notes in Theoretical Computer Science 295 (May 2013): 11–29. http://dx.doi.org/10.1016/j.entcs.2013.04.004.

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25

Perrot, Kévin, Pacôme Perrotin, and Sylvain Sené. "On Boolean Automata Networks (de)Composition1." Fundamenta Informaticae 181, no. 2-3 (2021): 163–88. http://dx.doi.org/10.3233/fi-2021-2055.

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Boolean automata networks (BANs) are a generalisation of Boolean cellular automata. In such, any theorem describing the way BANs compute information is a strong tool that can be applied to a wide range of models of computation. In this paper we explore a way of working with BANs which involves adding external inputs to the base model (via modules), and more importantly, a way to link networks together using the above mentioned inputs (via wirings). Our aim is to develop a powerful formalism for BAN (de)composition. We formulate three results: the first one shows that our modules/wirings defini

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26

Goles, Eric. "The complexity of some automata networks." Electronic Proceedings in Theoretical Computer Science 90 (August 13, 2012): 21–22. http://dx.doi.org/10.4204/eptcs.90.2.

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27

Neidig, Jorg, and Jan Lunze. "COORDINATED DIAGNOSIS OF NONDETERMINISTIC AUTOMATA NETWORKS." IFAC Proceedings Volumes 39, no. 13 (2006): 150–55. http://dx.doi.org/10.3182/20060829-4-cn-2909.00024.

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28

Rohani, Maryam, and Mohammad Hadi Afshar. "Sewer Networks Optimization Using Cellular Automata." Studies in Engineering and Technology 1, no. 1 (2013): 1. http://dx.doi.org/10.11114/set.v1i1.237.

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The Hybrid Cellular Automata (HCA) method is used in this paper for the optimal design of sewer network problems with the fixed layout. The HCA method decomposes the problem into two sub-problems with considering the pipe diameters and nodal cover depths as decision variables. Two stages are solved iteratively for determining the decision variables in a manner to minimize the total cost of the sewer network subject to the operational constraints. The HCA method is used to optimally solve three benchmark examples with different sizes and the results are presented and compared to those of the ex

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29

Nicopolitidis, Petros, Georgios Papadimitriou, Andreas Pomportsis, Panagiotis Sarigiannidis, and Mohammad Obaidat. "Adaptive wireless networks using learning automata." IEEE Wireless Communications 18, no. 2 (2011): 75–81. http://dx.doi.org/10.1109/mwc.2011.5751299.

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30

Allegretti, Dylan G., Garrett T. Kenyon, and William C. Priedhorsky. "Cellular Automata for Distributed Sensor Networks." International Journal of High Performance Computing Applications 22, no. 2 (2008): 167–76. http://dx.doi.org/10.1177/1094342007083803.

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31

de Sales, J. A., M. L. Martins, and D. A. Stariolo. "Cellular automata model for gene networks." Physical Review E 55, no. 3 (1997): 3262–70. http://dx.doi.org/10.1103/physreve.55.3262.

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32

Matamala, Martín, and Eric Goles. "Dynamic behavior of cyclic automata networks." Discrete Applied Mathematics 77, no. 2 (1997): 161–84. http://dx.doi.org/10.1016/s0166-218x(97)84104-2.

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33

Weisbuch, G. "Networks of automata and biological organization." Journal of Theoretical Biology 121, no. 3 (1986): 255–67. http://dx.doi.org/10.1016/s0022-5193(86)80106-0.

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34

Gusak, Oleg, Tuğrul Dayar, and Jean-Michel Fourneau. "Lumpable continuous-time stochastic automata networks." European Journal of Operational Research 148, no. 2 (2003): 436–51. http://dx.doi.org/10.1016/s0377-2217(02)00431-9.

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35

Goles, Eric, Pedro Montealegre, Ville Salo, and Ilkka Törmä. "PSPACE-completeness of majority automata networks." Theoretical Computer Science 609 (January 2016): 118–28. http://dx.doi.org/10.1016/j.tcs.2015.09.014.

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36

Boccara, Nino, Henryk Fukś, and Servie Geurten. "A new class of automata networks." Physica D: Nonlinear Phenomena 103, no. 1-4 (1997): 145–54. http://dx.doi.org/10.1016/s0167-2789(97)00257-1.

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37

Garzon, Max. "Cellular automata and discrete neural networks." Physica D: Nonlinear Phenomena 45, no. 1-3 (1990): 431–40. http://dx.doi.org/10.1016/0167-2789(90)90200-9.

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38

Siegelmann, Hava T. "RECURRENT NEURAL NETWORKS AND FINITE AUTOMATA." Computational Intelligence 12, no. 4 (1996): 567–74. http://dx.doi.org/10.1111/j.1467-8640.1996.tb00277.x.

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39

Berninghaus, Siegfried K., and Ulrich Schwalbe. "Conventions, local interaction, and automata networks." Journal of Evolutionary Economics 6, no. 3 (1996): 297–312. http://dx.doi.org/10.1007/s001910050024.

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40

Chan, Raymond H., and Wai Ki Ching. "Circulant preconditioners for stochastic automata networks." Numerische Mathematik 87, no. 1 (2000): 35–57. http://dx.doi.org/10.1007/s002110000173.

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Berninghaus, Siegfried K., and Ulrich Schwalbe. "Conventions, local interaction, and automata networks." Journal of Evolutionary Economics 6, no. 3 (1996): 297–312. http://dx.doi.org/10.1007/bf01193635.

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42

Calidonna, C. R., S. Di Gregorio, and M. Mango Furnari. "Mapping applications of cellular automata into applications of cellular automata networks." Computer Physics Communications 147, no. 1-2 (2002): 724–28. http://dx.doi.org/10.1016/s0010-4655(02)00385-5.

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43

Grachev, Petr, Sergey Muravyov, Andrey Filchenkov, and Anatoly Shalyto. "Automata generation based on recurrent neural networks and automated cauterization selection." Information and Control Systems, no. 1 (February 19, 2020): 34–43. http://dx.doi.org/10.31799/1684-8853-2020-1-34-43.

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Intoduction: The regular inference problem is to synthesize deterministic finite-state automata by a list of words which are examplesand counterexamples of some unknown regular language. This problem is one of the main in the theory of formal languages and relatedfields. One of the most successful solutions to this problem is training a recurrent neural network on word classification and clusteringthe vectors in the space of RNN inner weights. However, it is not guaranteed that a consistent automaton can be constructed based onthe clustering results. More complex models require more memory, tr

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44

Shafeie, Shekufeh, and M. R. Meybodi. "The Effects of Using Some Different Types of Learning Automata on Presentation of Adaptive Neighbor-Based and Energy Efficient Topology Control Protocols in Wireless Sensor Networks." Journal of Machine Intelligence 2, no. 2 (2017): 6–17. http://dx.doi.org/10.21174/jomi.v2i2.110.

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The high number of nodes and dynamic and periodic topological changes, as well as constraints in the physical size of nodes, energy resources, and power of processing are some characteristics of sensor networks that make them different from other networks. One method to overcome these constraints is topology control with the aim of reducing energy consumption and increasing the network’s capacity, which has the most influence on the network’s efficiency, especially in terms of energy consumption and lifetime. In consideration of learning Automata’s abilities, such as low computational load and

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Seifter, Jared, and James A. Reggia. "Lambda and the Edge of Chaos in Recurrent Neural Networks." Artificial Life 21, no. 1 (2015): 55–71. http://dx.doi.org/10.1162/artl_a_00152.

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The idea that there is an edge of chaos, a region in the space of dynamical systems having special meaning for complex living entities, has a long history in artificial life. The significance of this region was first emphasized in cellular automata models when a single simple measure, λCA, identified it as a transitional region between order and chaos. Here we introduce a parameter λNN that is inspired by λCA but is defined for recurrent neural networks. We show through a series of systematic computational experiments that λNN generally orders the dynamical behaviors of randomly connected/weig

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MOKDAD, LYNDA, and JALEL BEN-OTHMAN. "STOCHASTIC AUTOMATA NETWORKS FOR MODELLING SCHEDULING SCHEME IN WiMAX NETWORKS." Journal of Interconnection Networks 10, no. 04 (2009): 481–95. http://dx.doi.org/10.1142/s0219265909002674.

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WiMAX (Worldwide Interpretability for Microwave Access) is one of the leader standards of broadband Wireless Access (BWA). This standard becomes a competitor of 4G or LTE (Long Terme Evolution) with the introduction of different class of service flow (UGS, rtPS, nrtPS, BE). Each service class has its own QoS requirements. Even if the standard specify the transmission policy of each service class, it recommends the implementation of an admission control (AC) without any specification. This task is let to the operator. In this paper, we propose an implementation of an AC algorithm mechanism for

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47

Buchholz, Peter. "An Aggregation/Disaggregation Algorithm for Stochastic Automata Networks." Probability in the Engineering and Informational Sciences 11, no. 2 (1997): 229–53. http://dx.doi.org/10.1017/s0269964800004782.

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Stochastic automata networks (SANs) have recently received much attention in the literature as a means to analyze complex Markov chains in an efficient way. The main advantage of SANs over most other paradigms is that they allow a very compact description of the generator matrix by means of much smaller matrices for single automata. This representation can be exploited in different iterative techniques to compute the stationary solution. However, the set of applicable solution methods for SANs is restricted, because a solution method has to respect the specific representation of the generator

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48

Burgin, Mark, Rao Mikkilineni, and Giovanni Morana. "Intelligent organisation of semantic networks, DIME network architecture and grid automata." International Journal of Embedded Systems 8, no. 4 (2016): 352. http://dx.doi.org/10.1504/ijes.2016.077796.

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Alexander P, Buslaev, Tatashev Alexander G, and Yashina Marina V. "On cellular automata, traffic and dynamical systems in graphs." International Journal of Engineering & Technology 7, no. 2.28 (2018): 351. http://dx.doi.org/10.14419/ijet.v7i2.28.13210.

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Qualitative studies of discrete dynamical systems behavior on networks are relevant in many fields such as system biology, transportation, information traffic, material sciences and so on. We consider cellular automata on one-dimensional and two-dimensional toroidal supporters. At every discrete time moment, each cell of a cellular automaton is in one of two states 0 and 1. We introduce concept of the cellular automaton mass at fixed time. The cellular automaton mass is the quantity of cells such that these cells are in the state 1. The mass conservation law takes place if the mass of cellular

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Li, Cheng, Zhiyong Xu, Jingyuan Wang, et al. "Enhanced Clustering MAC Protocol Based on Learning Automata for UV Networks." Photonics 11, no. 4 (2024): 340. http://dx.doi.org/10.3390/photonics11040340.

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Ultraviolet (UV) networks are widely applied in complex electromagnetic environments. Designing an efficient multi-node medium access control (MAC) protocol for these networks is important. In this study, we proposed an enhanced clustering time division multiple access (TDMA) MAC protocol based on clustering and learning automata (LA). Subsequently, the effects of the network topology, class of service, and number of cluster nodes on the network performance under the proposed protocol were analyzed. Then, the protocol was compared with the TDMA protocol and clustering system. Results revealed

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