NDLI: A New Adaptive Fast Cellular Automaton Neighborhood Detection and Rule Identification Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Zhao, Y. Wei, H.L. Billings, S.A.
Copyright Year	1996
Abstract	An important step in the identification of cellular automata (CA) is to detect the correct neighborhood before parameter estimation. Many authors have suggested procedures based on the removal of redundant neighbors from a very large initial neighborhood one by one to find the real model, but this often induces ill conditioning and overfitting. This is true particularly for a large initial neighborhood where there are few significant terms, and this will be demonstrated by an example in this paper. By introducing a new criteria and three new techniques, this paper proposes a new adaptive fast CA orthogonal-least-square (Adaptive-FCA-OLS) algorithm, which cannot only adaptively search for the correct neighborhood without any preset tolerance but can also considerably reduce the computational complexity and memory usage. Several numerical examples demonstrate that the Adaptive-FCA-OLS algorithm has better robustness to noise and to the size of the initial neighborhood than other recently developed neighborhood detection methods in the identification of binary CA.
Page Count	5
File Size	224063
Starting Page	1283
Ending Page	1287
File Format	PDF
ISSN	10834419
Volume Number	42
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Automata Noise level Noise Sun Adaptation models Principal component analysis Polynomials neighborhood detection Cellular automata (CA) identification
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

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