• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.10 no.6
• /
• pp.1132-1136
• /
• 2006

In this paper, we analyze reachable/nonreachable states of 3-neighborhood cellular automata and propose the algorithm finding the predecessor of a reachable state.

• Journal of Korea Multimedia Society
• /
• v.18 no.11
• /
• pp.1281-1288
• /
• 2015

Nongroup Cellular Automata(CA) having two trees in the state transition diagram of a CA is suitable for pattern classifier which divides pattern set into two classes. Maji et al. [1] classified patterns by using multiple attractor cellular automata as a pattern classifier with dependency vector. In this paper we propose a method of generation of a pattern classifier using feature vector which is the extension of dependency vector. In addition, we propose methods for finding nonreachable states in the 0-tree of the state transition diagram of TPMACA corresponding to the given feature vector for the analysis of the state transition behavior of the generated pattern classifier.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2006.05a
• /
• pp.406-409
• /
• 2006

In this paper, we analyze reachable/nonreachable state of 3-neighborhood cellular automata and propose give the algorithm finding the predecessor of a reachable state.

• Journal of Korea Multimedia Society
• /
• v.4 no.1
• /
• pp.91-95
• /
• 2001

We investigate some properties of one dimensional linear nongroup cellular automata that have nonreachable states over GF(2). Specially we show interesting relationships between th states in a nonzero tree corresponding to each level state in the 0-tree.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.12
• /
• pp.2968-2974
• /
• 2015

Method for finding the attractors is the important object to investigate in the linear/additive CA because it is a primary interest in applications like pattern recognition, pattern classification, design of associative memory and query processing etc. But the research has been so far mostly concentrated around linear/additive CA and it is not enough to modelize the complex real life problem. So nonlinear CA is demanded to devise effective models of the problem and solutions around CA model. In this paper we introduce CLT as an upgraded version of RMT and provide the process for finding the attractors and nonreachable states effectively through the CLT.