Neighborhood Selection and Rules Identification for Cellular Automata: A Rough Sets Approach
详细信息 查看全文
- 作者：Bart?omiej P?aczek (19)
- 关键词：Rough sets ; Cellular automata ; Model identification
- 刊名：Lecture Notes in Computer Science
- 出版年：2014
- 出版时间：2014
- 年：2014
- 卷：1
- 期：1
- 页码：721-730
- 参考文献：1. Adamatzky, A.: Identification of Cellular Automata. T&F, London (1994)
2. Bazan, J., Szczuka, M.S.: The rough set exploration system. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 37-6. Springer, Heidelberg (2005) CrossRef
3. Billings, S., Yang, Y.: Identification of probabilistic cellular automata. IEEE Trans. Syst. Man Cybern. B Cybern. 33(2), 225-36 (2003) CrossRef
4. Chavoya, A., Duthen, Y.: Using a genetic algorithm to evolve cellular automata for 2D/3D computational development. In: Genetic and Evolutionary Computation Conference, pp. 231-32 (2006)
5. Craiu, R.V., Lee, T.C.M.: Pattern generation using likelihood inference for cellular automata. IEEE Trans. Image Process. 15(7), 1718-727 (2006) CrossRef
6. Feng, Y., Liu, Y., Tong, X., Liu, M., Deng, S.: Modeling dynamic urban growth using cellular automata and particle swarm optimization rules. Landscape Urban Plan. 102(3), 188-96 (2011) CrossRef
7. ?adniak, M., Piórkowski, A., M?ynarczuk, M.: The data exploration system for image processing based on server-side operations. In: Saeed, K., Chaki, R., Cortesi, A., Wierzchoń, S. (eds.) CISIM 2013. LNCS, vol. 8104, pp. 168-76. Springer, Heidelberg (2013)
8. Maeda, K.-I., Sakama, C.: Identifying cellular automata rules. J. Cell. Autom. 2(1), 1-0 (2007)
9. Maerivoet, S., De Moor, B.: Cellular automata models of road traffic. Phys. Rep. 419, 1-4 (2005) CrossRef
10. P?aczek, B.: Fuzzy cellular model for on-line traffic simulation. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds.) PPAM 2009, Part II. LNCS, vol. 6068, pp. 553-60. Springer, Heidelberg (2010)
11. P?aczek, B.: Rough sets in identification of cellular automata for medical image processing. J. Med. Inf. Technol. 22, 161-68 (2013)
12. Richards, F.C., Meyer, T.P., Packard, N.H.: Extracting cellular automaton rules directly from experimental data. Phys. D 45(1-), 189-02 (1990) CrossRef
13. Rosin, P.: Training cellular automata for image processing. IEEE Trans. Image Process. 15(7), 2076-087 (2006) CrossRef
14. Straatman, B., White, R., Engelen, G.: Towards an automatic calibration procedure for constrained cellular automata. Comput. Environ. Urban Syst. 28(1-), 149-70 (2004) CrossRef
15. Sun, X., Rosin, P.L., Martin, R.R.: Fast rule identification and neighborhood selection for cellular automata. IEEE Trans. Syst. Man Cybern. B Cybern. 41(3), 749-60 (2011) CrossRef
16. Was, J.: Cellular automata model of pedestrian dynamics for normal and evacuation conditions. In: 5th International Conference on Intelligent Systems Design and Applications, ISDA-5, pp. 154-59. IEEE Press (2005)
17. Yang, Y., Billings, S.: Extracting Boolean rules from CA patterns. IEEE Trans. Syst. Man Cybern. B Cybern. 30(4), 573-80 (2000) CrossRef
18. Zhao, Y., Billings, S.: The identification of cellular automata. J. Cell. Autom. 2(1), 47-5 (2007)
19. Coifman, B.: Time space diagrams for thirteen shock waves. Working Papers, California Partners for Advanced Transit and Highways (1997) - 作者单位：Bart?omiej P?aczek (19)
19. Institute of Computer Science, University of Silesia, B?dzińska 39, 41-200, Sosnowiec, Poland - ISSN：1611-3349
文摘
In this paper a method is proposed which uses data mining techniques based on rough sets theory to select neighborhood and determine update rule for cellular automata (CA). According to the proposed approach, neighborhood is detected by reducts calculations and a rule-learning algorithm is applied to induce a set of decision rules that define the evolution of CA. Experiments were performed with use of synthetic as well as real-world data sets. The results show that the introduced method allows identification of both deterministic and probabilistic CA-based models of real-world phenomena.