Open AccessCutting Process Model Design of Cutter Suction Dredger Based on Auto Regressive eXogenous and Radial Basis Function model
Author(s) -
Huan Zhang,
Miao Yu,
Yuan Wei
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2137/1/012064
Subject(s) - radial basis function , process (computing) , basis (linear algebra) , autoregressive model , suction , engineering , function (biology) , control theory (sociology) , computer science , radial basis function network , artificial neural network , mathematics , mechanical engineering , artificial intelligence , geometry , control (management) , evolutionary biology , econometrics , biology , operating system
The dredging operation of the strander dredger is complex, and the mathematical model established according to its key equipment characteristics is not possible to describe such a system having time degeneration and non-linear. Therefore, based on the analysis of mud formation process of dredger, RBF-ARX model is used to model the cutting process, and mud concentration is taken as the output. This modeling method is a combination model based on the theory of Auto-Regressive eXogenous (ARX) model and Gauss radial basis function (Radial Basis Function) neural network (RBF). The comparison between the simulation results and the actual data shows that the model can accurately describe the dynamic characteristics of cutter suction dredger in the cutting process.