Nonlinear Model Structure Detection Using Optimum Experimental Design and Orthogonal Least Squares  

Oleh:

Hong, X. ; Harris, C. J.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 12 no. 2 (2001), page 435-438.
Topik: edge detection; non linear; structure design; detection; optimum; experimental design; orthogonal; least squares

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.5 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the subset selection cost function includes an A ptimality design criterion to minimize the variance of the parameter estimates that ensures the adequacy and parsimony of the final model. An illustrative example is included to demonstrate the effectiveness of the new approach.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali