@inproceedings{9622e455e38c4167bdf8ab7fece819f9,
title = "An efficient identification scheme for nonlinear polynomial NARX model",
abstract = "Nonlinear polynomial NARX model identification often faces the problem of huge pool of candidate terms, which makes the evolutionary optimization based identification algorithm work with low efficiency. This paper proposes an efficient identification scheme with pre-processing to reduce the searching space effectively. Both the input selection and term selection are implemented to truncate the candidate pool with the help of correlation based orthogonal forward selection (COFS) algorithm and simplified orthogonal least square (OLS) algorithm, respectively. Then multi-objective evolutionary algorithm (MOEA) is used to identify the polynomial model in a relative small searching space.",
keywords = "Efficient, Input selection, Nonlinear polynomial model identification, Term selection",
author = "Yu Cheng and Miao Yu and Lan Wang and Jinglu Hu",
year = "2011",
month = dec,
day = "1",
language = "English",
isbn = "9784990288051",
series = "Proceedings of the 16th International Symposium on Artificial Life and Robotics, AROB 16th'11",
pages = "499--502",
booktitle = "Proceedings of the 16th International Symposium on Artificial Life and Robotics, AROB 16th'11",
note = "16th International Symposium on Artificial Life and Robotics, AROB '11 ; Conference date: 27-01-2011 Through 29-01-2011",
}