Casting the Swarms Problem in the Ensembles Context

Sürü robotları yüzlerce farklı şekilde modellenmiştir. Kalabalık olmaları sürülerin bir özelliğidir. Sayılamayacak kadar çok sayıya ulaştıklarında, termo-istatiksel mekanik devreye girebilir. Yazarlar bu avantajı kullanarak sürü robotları için evrensel istatistik oluşturmak istediler. Üç temel topluluk açıklandı ve formüle edildi. Sürüler izole edildiklerinde mikrokanonik uyum ortama hakim olurken, ortama av veya avcı girişi olur ise, duruma bağlı olarak değişimler gözlemlenir. Bu yüzden formulasyonlar ve geçişler şarta bağlıdır. Son olarak gözlemlenen olasılıklar tartışıldı

Sürüler Probleminin Topluluk Bağlamı Açısından Modellenmesi

Robotic swarms have been modeled in a myriad of ways. One property of the swarms is their multitude. As their numbers increase to uncountable numbers, the thermostatistical mechanics may come into play. Authors took advantage of this fact so as to generate global statistics for the swarm. Three distinct ensembles are explained and formulated. When isolated, the swarms behave as if microcanonical ensemble reigns. But when a predator or a prey appears, transitions are observed depending on the conditions. Therefore, both the formulations and the transitions are all contingent. Finally, observed probabilities were discussed

___

1.Rauch,E.M.,Millonas,M.M.,Chialvo,D.R., 1995.Pattern Formation and Functionality in Swarm Models,Physics Letters A207, no. 3-4: 185-93. doi:10.1016/0375-9601(95)00624-c.

2.Martinoli,A.,Easton,K.,Agassounon,W., 2004.Modeling Swarm Robotic Systems: A Case Study in Collaborative Distributed Manipulation,Int J Robot Res The International Journal of Robotics Research23, no. 4: 415-36. doi:10.1177/0278364904042197.

3.Chen,S.,Fang,H., 2006.Modeling and Stability Analysis of Social Foraging Swarms in Multi-obstacle Environment. J. Control Theory Appl,Journal of Control Theory and Applications4, no. 4: 343-48. doi:10.1007/s11768-006-5170-8.

4.Arlotti,L.,Deutsch,A.,Lachowicz,M., 2005.A Discrete Boltzmann-type Model of Swarming,Mathematical and Computer Modelling41, no. 10: 1193-201. doi: 10.1016/j.mcm.2005.05.011.

5.Zhao,Y.,Zu,W.,Zeng,H., 2009.A Modified Particle Swarm Optimization via Particle Visual Modeling Analysis,Computers & Mathematics with Applications57, no. 11-12: 2022-029. doi: 10.1016/j.camwa.2008.10.007.

6.Lin,Y.,Chang,W.,Hsieh,J., 2008.A Particle Swarm Optimization Approach to Nonlinear Rational Filter Modeling, Expert Systems with Applications34, no. 2: 1194-199. doi: 10.1016/j.eswa.2006.12.004.

7.Wu,Q.,2010.A Hybrid-forecasting Model Based on Gaussian Support Vector Machine and Chaotic Particle Swarm Optimization,Expert Systems with Applications37, no. 3: 2388-394. doi: 10.1016/j.eswa.2009.07.057.

8.Chan,K.Y.,Dillon,T.S.,Kwong,C.K., 2011.Polynomial Modeling for Time-varying Systems Based on a Particle Swarm Optimization Algorithm,Information Sciences181, no. 9: 1623-640. doi: 10.1016/j.ins.2011.01.006.

9.Cleghorn,C.W.,Engelbrecht,A.P., 2014.A Generalized Theoretical Deterministic Particle Swarm Model,Swarm Intell Swarm Intelligence8, no. 1: 35-59. doi:10.1007/s11721-013-0090-y.

10.Zhang,J.,2013.Canonical Ensemble Model for the Black Hole Quantum Tunneling Radiation,Chinese Physics Letters Chinese Phys. Lett.30, no. 7: 070401. doi:10.1088/0256-307x/30/7/070401.

11.Sierra,G.,Román,J.M.,Dukelsky,J., 2004.The Elementary Excitations of the Bcs Model in the Canonical Ensemble,International Journal of Modern Physics A Int. J. Mod. Phys. A19, no. Supp02: 381-95. doi:10.1142/s0217751x04020531.

12.Zhang,J., 2014.Canonical Ensemble Model for Black Hole Radiation,J.Astrophys Astron Journal of Astrophysics and Astronomy35, no. 3: 573-75. doi:10.1007/s12036-014-9290-0.

13.Nogawa,T.,Ito,N.,Watanabe,H., 2011.Evaporation-condensation Transition of the Two-dimensional Potts Model in the MicrocanonicalEnsemble,Physical Review E Phys. Rev. E84, no. 6. doi:10.1103/physreve.84.061107.

14.Wang,J.,Yang,T., 1996.Numerical Microcanonical Ensemble Method for Calculation on Statistical Models with Large Lattice Sizes,Phys. Rev. B Physical Review B54, no. 19: 13635-3642. doi:10.1103/physrevb.54.13635.

15.Hilbert,S.,Dunkel,J., 2006.Nonanalytic Microscopic Phase Transitions and Temperature Oscillations in the Microcanonical Ensemble: An Exactly Solvable One-dimensional Model for Evaporation,Physical Review E Phys. Rev. E74, no. 1. doi:10.1103/physreve.74.011120.

16.Alkhimov,V.I., 2014.A D-dimensional Model of the Canonical Ensemble of Open Strings,Theoretical and Mathematical Physics Theor Math Phys180, no. 1: 862-79. doi:10.1007/s11232-014-0185-7.

17.Knani,S.,Khalfaoui,M.,Hachicha,M.A.,Ben,Lamine,A.,Mathlouthi,M., 2012.Modelling of Water Vapour Adsorption on Foods Products by a Statistical Physics Treatment Using the Grand Canonical Ensemble,Food Chemistry132, no. 4: 1686-692. doi: 10.1016/j.foodchem.2011.11.065.

18.Knani,S.,Mathlouthi,M.,Ben Lamine,A., 2007.Modeling of the Psychophysical Response Curves Using the Grand Canonical Ensemble in Statistical Physics,Food Biophysics2, no. 4: 183-92. doi:10.1007/s11483-007-9042-7.

19.William C., Kalmykov, Yu.P.,Waldron,J.T., 1996.The Langevin Equation: With Applications in Physics, Chemistry, and Electrical Engineering,Singapore: World Scientific.

20.Sethna, James,P., 2006.Statistical Mechanics: Entropy, Order Parameters, and Complexity,Oxford, UK: New York.

21.Tsallis, C., 2009.Introduction to Nonextensive Statistical Mechanics: Approaching a Complex World,New York: Springer.

22.Bowley, R., Sánchez,M.,1996.Introductory Statistical Mechanics,Oxford: Clarendon Press.

23.Balian,R., 1991.From Microphysics to Macrophysics: Methods and Applications of Statistical Physics,Berlin: Springer-Verlag.
Çukurova Üniversitesi Mühendislik-Mimarlik Fakültesi Dergisi-Cover
  • ISSN: 1019-1011
  • Yayın Aralığı: 4
  • Başlangıç: 1986
  • Yayıncı: ÇUKUROVA ÜNİVERSİTESİ MÜHENDİSLİK FAKÜLTESİ
Sayıdaki Diğer Makaleler

Gemilerdeki Kaynaklı Yapılarda Isı Yalıtımı

Mehmet ŞAHİN, YAHYA BOZKURT

Determining the Optimum Application Recipe forMicrocapsules of Ozonated VegetableOils to Save Antibacterial Activity to Textiles

BURCU SANCAR BEŞEN, ONUR BALCI, Cem GÜNEŞOĞLU, İ. İrem TATLI, MEHMET ORHAN, A Erdem BEYLİ

Tufanbeyli Linyitlerinin Mineral Madde İçeriğinin İnteraktive Rietveld Temelli X-Işını Difraksiyonu Yöntemi ile Kantitatif Olarak İncelenmesi

ABDULKADİR ÜRÜNVEREN, SUPHİ URAL

Saplama Kaynak Bağlantılarının Çekme Dayanımının ANFIS ile Modellenmesi

NECİP FAZIL YILMAZ, M Veysel ÇAKIR, MUSA YILMAZ

Production, Characterization and Effect of Te Doping on FeSe-11 Compounds

Derya FARİSOĞULLARI, Nilay KANTARCI GÜLER, FARUK KARADAĞ, Ahmet EKİCİBİL, Bekir ÖZÇELİK

Kondansatör Deşarjli Saplama Kaynağı Kaynak Voltunun Aa6082 Alüminyum Alaşımında Birleşmeye Etkisi

MEHMET ÇAKMAKKAYA, AHMET YÖNETKEN, Ayhan EROL

The Usability of 3DFlattening in Design and Pattern Preparation of Tight-Fit Garments

Derya TAMA, Arzu ŞEN KILIÇ, ZİYNET ÖNDOĞAN, Selçuk NİZAMOĞLU

Experimental Investigation of Low-Velocity Impact Response of Plain-Weave Glass/Epoxy Composites Reinforced with Carbon Nanotubes

GÜLŞAH ÖNER, Hasan Yavuz ÜNAL, Yeliz PEKBEY

Yeni Bir Eddy Akımı Ayrıştırıcısı ile Küçük Boyutlu Demirsiz Metallerin Ayrıştırılması

AHMET FENERCİOĞLU, Hamit BARUTÇU

Orbital Tig Kaynak Yöntemiyle Kaynak Edilmiş Dubleks Paslanmaz Çeliklerin Mekanik, Metalurjik ve Korozyon Özellikleri

Umut SÖNMEZ, Niyazi ÇAVUŞOĞLU, Vural CEYHUN