The identification of nonlinear dynamic systems using linear-in-the-parameters models is
studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and …

The purpose of variable selection is to pre-select a subset consisting of the significant
variables or to eliminate the redundant variables from all the candidate variables of a system …

In this paper, we attempt to address the architectural design of ELM regressor by applying a
constructive method on the basis of ELM algorithm. After the nonlinearities of ELM network …

This paper investigates the two-stage stepwise identification for a class of nonlinear dynamic
systems that can be described by linear-in-the-parameters models, and the model has to be …

The key point in design of radial basis function networks is to specify the number and the
locations of the centers. Several heuristic hybrid learning methods, which apply a clustering …

Model structure selection plays a key role in non-linear system identification. The first step in
non-linear system identification is to determine which model terms should be included in the …

Extreme learning machine (ELM) proposed by Huang et al. was developed for generalized
single hidden layer feedforward networks (SLFNs) with a wide variety of hidden nodes. It …

A new adaptive orthogonal search (AOS) algorithm is proposed for model subset selection
and non-linear system identification. Model structure detection is a key step in any system …

A new hybrid model structure combing polynomial models with multiresolution wavelet
decompositions is introduced for nonlinear system identification. Polynomial models play an …

We investigate the effective connectivity in the lateral geniculate nucleus and visual cortex of
humans with amblyopia. Six amblyopes participated in this study. Standard retinotopic …