An Orthogonal Forward Regression Algorithm Combined with Basis Pursuit and D-optimality

نویسندگان

  • X. Hong
  • S. Chen
  • C. J. Harris
چکیده

A new forward regression model identification algorithm is introduced. The derived model parameters, in each forward regression step, are initially estimated via orthogonal least squares (OLS) (using the modified Gram-Schmidt procedure), followed by being tuned with a new gradient descent learning algorithm based on the basis pursuit that minimizes the norm of the parameter estimate vector. The model subset selection cost function includes a D-optimality design criterion. Both the parameter tuning procedure, based on basis pursuit, and the model selection criterion, based on the D-optimality that is effective in ensuring model robustness, are integrated with the forward regression, so as to maintain computational efficiency. An illustrative example is included to demonstrate the effectiveness of the new approach.

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تاریخ انتشار 2003