Analysis of Mixed-Rule Cellular Automata Based on Simple Feature Quantities

被引：1
作者：
Tada, Naoki [1 ]
Saito, Toshimichi [1 ]
机构：
[1] Hosei Univ, Koganei, Tokyo 1848584, Japan
来源：
NEURAL INFORMATION PROCESSING, PT II | 2015年 / 9490卷
关键词：
Cellular automata; Digital return maps; Feature quantities;
D O I：
10.1007/978-3-319-26535-3_55
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper studies cellular automata with mixed rules (MCA) that can generate various spatiotemporal patterns. The dynamics is integrated into the digital return map on a set of lattice points. In order to analyze the dynamics, we present two simple feature quantities of steady state and transient phenomena. In elementary numerical experiments, plentifulness of the dynamics is confirmed.
引用
收藏
页码：484 / 491
页数：8
相关论文
共 11 条
  • [1] AMARI SI, 1974, KYBERNETIK, V14, P201
  • [2] Chua L. O., 2005, NONLINEAR DYNAMICS P, VII
  • [3] Chua L.O, 2005, NONLINEAR DYNAMICS P, VI
  • [4] Lohn J. D., 1997, IEEE Transactions on Evolutionary Computation, V1, P165, DOI 10.1109/4235.661547
  • [5] Moriyasu Jungo, 2014, Artificial Neural Networks and Machine Learning - ICANN 2014. 24th International Conference on Artificial Neural Networks. Proceedings: LNCS 8681, P611, DOI 10.1007/978-3-319-11179-7_77
  • [6] Training cellular automata for image processing
    Rosin, Paul L.
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2006, 15 (07) : 2076 - 2087
  • [7] Sawayama R, 2014, LECT NOTES COMPUT SC, V8835, P271, DOI 10.1007/978-3-319-12640-1_33
  • [8] Block cipher based on reversible cellular automata
    Seredynski, M
    Bouvry, P
    [J]. CEC2004: PROCEEDINGS OF THE 2004 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2, 2004, : 2138 - 2143
  • [9] Completely reproducible description of digital sound data with cellular automata
    Wada, M
    Kuroiwa, J
    Nara, S
    [J]. PHYSICS LETTERS A, 2002, 306 (2-3) : 110 - 115
  • [10] Yamaoka Hiroki, 2014, Artificial Neural Networks and Machine Learning - ICANN 2014. 24th International Conference on Artificial Neural Networks. Proceedings: LNCS 8681, P73, DOI 10.1007/978-3-319-11179-7_10
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