Affiliations: [a] School of Mechanical Engineering, Beijing Institute of Technology, Beijing, Peolple’s Republic of China | [b] Department of Mechanical Engineering, Texas A&M University, USA
Correspondence:
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Corresponding author. Liang Gu, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, People’s Republic of China. E-mail: [email protected]
Abstract: System modeling is one of the most important tasks of dynamic analysis and prediction systems, and imprecise model may lead to high bias. The presence of noise in sample data can make it more difficult to obtain precise system models. A new modeling algorithm called ANFIS-GMDH is presented in this paper, which builds upon the traditional ANFIS structure and utilizes the self-organizing mechanisms of GMDH. The aim of ANFIS-GMDH is to improve upon the traditional ANFIS method and prevent overfitting of noisy data. The well-studied Box-Jenkins gas furnace data is utilized to validate the algorithm, with results showing that the proposed algorithm performs better than traditional ANFIS, GMDH and subtractive clustering for both noisy and noiseless data, without any significant increase in execution time.
Keywords: ANFIS-GMDH, noisy data, system modeling, gas furnace data
