System identification: theory for the user
System identification: theory for the user
System identification
Structure identification of nonlinear dynamic systems—a survey on input/output approaches
Automatica (Journal of IFAC)
Twenty-one ML estimators for model selection
Automatica (Journal of IFAC)
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Advances in Engineering Software
Fast orthogonal least squares algorithm for efficient subset modelselection
IEEE Transactions on Signal Processing
A two-stage algorithm for identification of nonlinear dynamic systems
Automatica (Journal of IFAC)
On the efficiency of the orthogonal least squares training method for radial basis function networks
IEEE Transactions on Neural Networks
A Hybrid Forward Algorithm for RBF Neural Network Construction
IEEE Transactions on Neural Networks
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Signal transduction pathways describe the dynamics of cellular response to input signalling molecules at receptors on the cell membrane. The Mitogen-Activated Protein Kinase (MAPK) cascade is one of such pathways that are involved in many important cellular processes including cell growth and proliferation. This paper describes a black-box model of this pathway created using an advanced two-stage identification algorithm. Identification allows us to capture the unique features and dynamics of the pathway and also opens up the possibility of regulatory control design. In the approach described, an optimal model is obtained by performing model subset selection in two stages, where the terms are first determined by a forward selection method and then modified using a backward selection model refinement. The simulation results demonstrate that the model selected using the two-stage algorithm performs better than with the forward selection method alone.