Aiming at the problem that the three-phase APF’s dynamic model is a multi-variable, nonlinear and strong coupling system, an internal model controller for three-phase APF based on LS-Extreme Learning Machine is studied in this paper. As a novel single hidden layer feed-forward neural networks, extreme learning machine (ELM) has several advantages: simple net structural, fast learning speed, goo...

Material properties are very important in most mechanical engineering computations. Numerous approaches have been proposed to estimate these material properties such as State of Equations, Statistical Regression, and Neural Networks modeling schemes. Unfortunately, accuracy of some of these earlier approaches is often limited. Recently, extreme learning machine has been proposed as a new comput...

Machine Learning of Natural Language provides a rich environment for exploring supervised and unsupervised learning techniques including soft clustering and rough sets. This keynote presentation will trace the course of our Natural Language Learning as well as some quite intriguing spin-off applications. The focus of the paper will be learning, by both human and computer, reinterpreting our wor...

Maximum margin clustering (MMC) is a newly proposed clustering method, which extends large margin computation of support vector machine (SVM) to unsupervised learning. But in nonlinear cases, time complexity is still high. Since extreme learning machine (ELM) has achieved similar generalization performance at much faster learning speed than traditional SVM and LS-SVM, we propose an extreme maxi...

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coeffi...