A new approach founded on Radial Basis Functions (RBF) and Partial Least Squares (PLS) is proposed to model non-linear chemical systems. Its performance is demonstrated for two simulated examples and compared with those of Multilayer Feedforward Network (MLP), Radial Basis Function Network (RBFN), a
Multifit: a flexible non-linear least squares regression program in BASIC
β Scribed by A.R. Walmsley; A.G. Lowe
- Book ID
- 113290806
- Publisher
- Elsevier Science
- Year
- 1985
- Tongue
- English
- Weight
- 312 KB
- Volume
- 21
- Category
- Article
- ISSN
- 0169-2607
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This note considers a paradox arising in the least-squares estimation of linear regression models in which the error terms are assumed to be i.i.d. and possess ΓΏnite rth moment, for r β [1; 2). We give a concrete example to show that the least-squares estimator of the slope parameter is inconsistent