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Title: Generating training data for identifying neurofuzzy models of non-linear dynamic systems
Authors: Zhou, Yimin
Dexter, Arthur
Zolotas, Argyrios C.
Issue Date: 2009
Publisher: © IEEE
Citation: ZHOU, Y., DEXTER, A. and ZOLOTAS, A.C., 2009. Generating training data for identifying neurofuzzy models of non-linear dynamic systems. IN: Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, Shanghai, China, Dec. 16-18, pp. 6738 - 6743
Abstract: This paper presents a methodology for generating data for training a fuzzy relational model, one neuro-fuzzy modeling technique. Neuro-fuzzy modeling is a popular à ¿grey-boxà ¿ modeling technique used to model complex, non-linear plants utilizing input-output data, i.e. as an alternative to physical-based modeling. The controllable input variables of each of the generated training data set, are positioned at the centres of the fuzzy sets, so that the steady-state and dynamic performance of the model should be satisfactory whenever the control signal is stepped between the centres of its fuzzy sets. The rule confidences of the fuzzy rules are identified via the Global Least-Square (GLS) identification algorithm. The model performance is validated by using a simulated water level control system.
Description: This is a conference paper [© IEEE]. It is also available at: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Version: Published
DOI: 10.1109/CDC.2009.5400931
URI: https://dspace.lboro.ac.uk/2134/6138
ISBN: 9781424438716
ISSN: 0191-2216
Appears in Collections:Conference Papers and Presentations (Mechanical, Electrical and Manufacturing Engineering)

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