Multi-region System Modelling by using Genetic Programming to Extract Rule Consequent Functions in a TSK Fuzzy System

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Zhang:2019:ICARM,

• author = "Yu Zhang3 and Miguel Martinez-Garcia and Jose R. Serrano-Cruz and Anthony Latimer",
• title = "Multi-region System Modelling by using Genetic Programming to Extract Rule Consequent Functions in a {TSK} Fuzzy System",
• booktitle = "2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM)",
• year = "2019",
• pages = "987--992",
• month = jul,
• keywords = "genetic algorithms, genetic programming, Fuzzy",
• DOI = "doi:10.1109/ICARM.2019.8834163",
• abstract = "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.",
• notes = "Also known as \cite{8834163}",

}

Genetic Programming entries for Yu Zhang3 Miguel Martinez-Garcia Jose R Serrano-Cruz Anthony Latimer

Citations