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12 - Aeronautical, Mechanical, Chemical and Manufacturing Engineering

Cranfield University

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Output 27 of 564 in the submission

Article title

A new adaptive fast cellular automaton neighborhood detection and rule identification algorithm

Type

D - Journal article

DOI

10.1109/TSMCB.2012.2185790

Title of journal

IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics

Article number

Volume number

Issue number

First page of article

1283

ISSN of journal

1083-4419

Year of publication

2012

URL

http://www.scopus.com/inward/record.url?partnerID=yv4JPVwI&eid=2-s2.0-84864146863&md5=374a7bb67d71f1cfb7959122c1d51eac

Number of additional authors

Additional information

Supported by EPSRC Platform Grant (EP/H00453X/1 £1.2M 2010-2015), this paper introduces a novel algorithm that significantly reduces computational complexity for Cellular Automaton modelling meanwhile prevents ill conditioning and over fitting. This algorithm has been applied to Irritable Bowel Syndrome data analysis in collaboration with Dr BM Corfe (Department of Surgical Oncology, University of Sheffield, email: b.m.corfe@shef.ac.uk), based on which a research grant proposal has been submitted to Yorkshire Cancer Research (£80,278). It has also facilitated the research of the EPSRC grant titled “Spatio-Temporal Systems Estimation, Modelling and Analysis” (EP/G042209/1, £480K 01/2010-06/2013).

Interdisciplinary

Cross-referral requested

Research group

B - Computational Science & Engineering

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract