A demand for predictive models for on-line estimation of variables is increasing in industry.
As industrial processes are time-varying, on-line learning algorithms should be adaptive to …

Many real world applications are of time‐varying nature and an online learning algorithm is
preferred in tracking the real‐time changes of the time‐varying system. Online sequential …

Abstract Huang et al.(2004) has recently proposed an on-line sequential ELM (OS-ELM) that
enables the extreme learning machine (ELM) to train data one-by-one as well as chunk-by …

In order to more accurately model time-varying nonlinear systems, we propose a regularized
online sequential extreme learning machine with adaptive regulation factor (ROSELM-ARF) …

Incremental extreme learning machine (I-ELM) randomly obtains the input weights and the
hidden layer neuron bias during the training process. Some hidden nodes in the ELM play a …

Extreme learning machine (ELM) is an emerging machine learning algorithm for training
single-hidden-layer feedforward networks (SLFNs). The salient features of ELM are that its …

Single-hidden-layer feedforward networks with randomly generated additive or radial basis
function hidden nodes have been theoretically proved that they can approximate any …

In this paper, two novel Multilayer Extreme Learning Machine (ML-ELM) networks are
presented. We call them Improved Multilayer Extreme Learning Machines (IML-ELM). The …

The extreme learning machine (ELM) has been extensively studied in the machine learning
field and has been widely implemented due to its simplified algorithm and reduced …

It is well known that the architecture of the extreme learning machine (ELM) significantly
affects its performance and how to determine a suitable set of hidden neurons is recognized …