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Multi-region system modelling by using genetic programming to extract rule consequent functions in a TSK fuzzy system

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conference contribution
posted on 03.08.2021, 14:53 by Eve ZhangEve Zhang, Miguel Martinez-GarciaMiguel Martinez-Garcia, Jose R Serrano-Cruz, Anthony Latimer
This paper aims to build a fuzzy system by means of genetic programming, which is used to extract the relevant function for each rule consequent through symbolic regression. The employed TSK fuzzy system is complemented with a variational Bayesian Gaussian mixture clustering method, which identifies the domain partition, simultaneously specifying the number of rules as well as the parameters in the fuzzy sets. The genetic programming approach is accompanied with an orthogonal least square algorithm, to extract robust rule consequent functions for the fuzzy system. The proposed model is validated with a synthetic surface, and then with real data from a gas turbine compressor map case, which is compared with an adaptive neuro-fuzzy inference system model. The results have demonstrated the efficacy of the proposed approach for modelling system with small data or bifurcating dynamics, where the analytical equations are not available, such as those in a typical industrial setting.

Funding

EPSRC Grant EVES (EP/R029741/1)

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM)

Pages

987 - 992

Source

2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM)

Publisher

IEEE

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Publication date

2019-09-12

Copyright date

2019

ISBN

9781728100647

Language

en

Location

Toyonaka, Japan

Event dates

3rd July 2019 - 5th July 2019

Depositor

Dr Eve Zhang. Deposit date: 29 July 2021