A new way to use fuzzy inference systems in activity-based cellular modeling simulations

Over the last few years, both the study and the design of IT implementations of Cellular Automata Models (CAM) have gained a renewed interest. The success of these models in the Theory of Modelling and Simulation (TMS) relies on the structural phenomenon of emergence which makes it possible to run realistic simulations, despite lacking a modeling process for real systems. Cellular Automata Models (CAMs)do not describe real systems with complex equations, they allow the complexity of real systems to emerge from simple interactions described locally from their cellular elements. In order to optimize simulations whatever the spatial dimension considered, the concept of activity is used. In this work, we introduce disturbances in propagation rules and we improve simulation rendering. We express a doubt in the expression of the cell's activity, i.e. we express the activity rule by means of an Fuzzy Inference System (FIS). We present a new way to use Fuzzy Inference System (FIS), in an activity-based cellular modeling approach for fire spreading simulations.

[1]  Paul-Antoine Bisgambiglia,et al.  Cognitive simulation-based on knowledge evolution in fuzzy discrete event systems , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

[2]  Ilan Vertinsky,et al.  An object-oriented cellular automata model for forest planning problems , 2008 .

[3]  Yi Sun,et al.  High performance simulation of devs based large scale cellular space models , 2009 .

[4]  Xiaolin Hu,et al.  Spatial activity-based modeling for pedestrian crowd simulation , 2013, Simul..

[5]  Stephen Wolfram,et al.  A New Kind of Science , 2003, Artificial Life.

[6]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  Jean François Santucci,et al.  iDEVS: New method to study inaccurate systems , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[8]  Paola Flocchini,et al.  On the relationship between fuzzy and Boolean cellular automata , 2011, Theor. Comput. Sci..

[9]  Paul-Antoine Bisgambiglia,et al.  A Polyphony Learning Software (PLS) based on gesture recognition by fuzzy inference system , 2015, J. Intell. Fuzzy Syst..

[10]  Bernard P. Zeigler,et al.  Continuity and Change (Activity) Are Fundamentally Related in DEVS Simulation of Continuous Systems , 2004, AIS.

[11]  Eric Innocenti,et al.  Randomizing activity in fire spreading simulations , 2013 .

[12]  Bin Chen,et al.  Activity-based simulation using DEVS: increasing performance by an activity model in parallel DEVS simulation , 2014, Journal of Zhejiang University SCIENCE C.

[13]  Nikos E. Mastorakis,et al.  A Cellular Automata Model for Fire Spreading Prediction , 2010 .

[14]  Bernard P. Zeigler,et al.  Modeling and simulation of fire spreading through the activity tracking paradigm , 2008 .

[15]  Salim Hariri,et al.  ACTIVITY CHARACTERIZATION OF SPATIAL MODELS: APPLICATION TO DISCRETE EVENT SOLUTION OF PARTIAL DIFFERENTIAL EQUATIONS By , 2003 .

[16]  E. H. Mamdani,et al.  Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis , 1976, IEEE Transactions on Computers.

[17]  Paul-Antoine Bisgambiglia,et al.  Plateforme DEVS multicomposant dédiéè a la gestion des pêcheries , 2016 .

[18]  John W. Sutherland,et al.  General Systems Theory: Foundations, Developments, Applications , 1974 .

[19]  Stefania Bandini,et al.  Cellular automata: From a theoretical parallel computational model to its application to complex systems , 2001, Parallel Comput..

[20]  A. Adamatzky.  Hierarchy of fuzzy cellular automata , 1994 .

[21]  S. Di Gregorio,et al.  A Model for the Simulation of Forest Fire Dynamics Using Cellular Automata , 2006 .

[22]  David R. C. Hill,et al.  Design of a Multithreaded Parallel Model for Fire Spread , 2003 .

[23]  Eric Innocenti,et al.  WinGPYRO: A software platform for kinetic study of forest fuels , 2013 .

[24]  G. Mauri,et al.  Cellular automata in fuzzy backgrounds , 1997 .

[25]  Laurent Capocchi,et al.  Fuzzy inference models for Discrete EVent systems , 2010, International Conference on Fuzzy Systems.

[26]  A. Boukerche,et al.  Exploiting the Concept of Activity for Dynamic Reconfiguration of Distributed Simulation , 2007, 11th IEEE International Symposium on Distributed Simulation and Real-Time Applications (DS-RT'07).

[27]  Jean François Santucci,et al.  Fuzzy simulation for discrete events systems , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[28]  Rob Aspin,et al.  Augmenting the CAVE: An Initial Study into Close Focused, Inward Looking, Exploration in IPT Systems , 2007 .

[29]  Wan Kwon,et al.  Fuzzy-DEVS Formalism : Concepts , Realization and ApplicationsYi , 2007 .

[30]  Feng Lin,et al.  Fuzzy discrete event systems and their observability , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[31]  Bernard P. Zeigler,et al.  Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems , 2000 .

[32]  Jean François Santucci,et al.  Active-DEVS: a computational model for the simulation of forest fire propagation , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).