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. Author manuscript; available in PMC: 2019 Feb 1.
Published in final edited form as: Control Eng Pract. 2018 Feb;71:129–141. doi: 10.1016/j.conengprac.2017.10.013

Table 3. Comparison of candidate models.

Models Strengths Weaknesses
RLS Simplicity of calibration;
Generalization;
Rapid convergence;
Better performance for limited training data;		 Poorer tracking performance for process with time-varying parameters;
Disability for nonlinear process;
(with respect to the other three methods)
EX-RLS Good tracking performance for linear process with time-varying parameters;
Applicable to model uncertainty;		 Lack of flexibility;
Disability for nonlinear process;
Sensitive to parameters;
KRLS Ability to represent nonlinear problems; Computation complexity increases with the training data size;
Need more training data to approximate a nonlinear process;
EX-KRLS Better tracking performance for dynamic nonlinear process;
Ability of estimating nonlinear state space model;
Ability of modeling nonlinear systems with slow time-variant states;		 Computation complexity increases with the training data size;
More training data is needed to approximate the nonlinear process;
Sensitive to parameters;
Too complex in case of the model order is higher;