considering the importance of cd and u as pollutants of the environment, this study aims to predict the concentrations of these elements in a stream sediment from the eshtehard region in iran by means of a developed artificial neural network (ann) model. the forward selection (fs) method is used to select the input variables and develop hybrid models by ann. from 45 input candidates, 13 and 14 ...

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...

Xia Hong, Sheng Chen, Yi Guo and Junbin Gao Department of Computer Science, School of Mathematical, Physical and Computational Sciences, University of Reading, Reading, UK; Electronics and Computer Science, University of Southampton, Southampton, UK; Department of Electrical and Comptuer Engineering , Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia; CSIRO Mathematics and...

An efficient two-level model identification method aiming at maximising a model's generalisation capability is proposed for a large class of linear-in-the-parameters models from the observational data. A new elastic net orthogonal forward regression (ENOFR) algorithm is employed at the lower level to carry out simultaneous model selection and elastic net parameter estimation. The two regularisa...

A novel training algorithm for nonlinear discriminants for classification and regression in Reproducing Kernel Hilbert Spaces (RKHSs) is presented. It is shown how the overdetermined linear leastsquares-problem in the corresponding RKHS may be solved within a greedy forward selection scheme by updating the pseudoinverse in an order-recursive way. The described construction of the pseudoinverse ...