Guest editorial: Special issue on Extreme learning machine and applications (II)

Abstract

It is our great pleasure to present this special issue on Extreme learning machine (ELM) and applications (II) to all NCA readers and neural computing society. Our initial schedule was to publish the special issue on ELM and applications with about 15 articles and to report the recent development of ELM theory and applications as well as the challenges in designing, analysing and implementing the algorithms and systems. However, after rigorously reviewing all of received papers on the basis of innovativeness and relevance for all NCA readers, we realized that many of them were high quality and finally selected 30 top papers. After getting the permission from Editor-inChief, Professor John MacIntyre, we are very happy to present these selected top papers in two special issues. The following is the brief introduction of 15 articles in the special issue: Extreme Learning Machine and Applications (II). In ‘‘Fault detection and diagnosis method for batch process based on ELM-based fault feature phase identification’’, the authors propose a fault detection and diagnosis algorithm for batch processes with ELM methodology. In this work, ELM is first utilized to identify the feature phases for each fault, and the whole batch is divided into a few ‘‘short stages’’. The multiway Fisher discriminant analysis (MFDA) models are then built for these divided ‘‘short stages’’ to perform fault detection and diagnosis for a batch process. The simulation results have shown the excellent performance of the algorithm for the fault detection in a hydrostatic testing process. In ‘‘Multiplekernel-learning-based extreme learning machine for classification design’’, the authors develop two multiple kernel classifiers. The first one is based on a convex combination of the given base kernels, while the second one uses a convex combination of the so-called equivalent kernels. Experimental results have shown that, for a large number of data sets, the proposed classifiers are fast, accurate and easily trained. In ‘‘Variational Bayesian extreme learning machine’’, the authors present a Bayesian probabilistic model based on ELM to avoid the ill-posed problem in input–hidden node matrix. Both the regression experiments and classification experiments have clearly shown the excellent performance compared with a few existing ELMbased schemes. In ‘‘A recurrent neural network for modelling crack growth of aluminium alloy’’, the authors develop a new recurrent neural model for crack growth process of aluminium alloy. It has been shown that a recurrent neural network with the feedback loops at the output layer is constructed to model the dynamic relationship between the crack growth and cyclic stress excitations of aluminium alloy. The extreme learning machine is then used to uniformly randomly assign the input weights in a proper range and globally optimize both the output weights and feedback parameters, to ensure that the dynamics of crack growth under variable amplitude loading can be accurately modelled. The simulations with experimental data have shown excellent results. In ‘‘Extend semi-supervised ELM and a frame work’’, the authors analyse the semi-supervised ELM (SELM) indepth and propose an extended SELM algorithm that can efficiently solve the classification problems with a small number of labelled samples. The simulation results have shown excellent classification performance compared with both ELM and SELM algorithms. In ‘‘Feature adaptive online sequential extreme learning machine for lifelong & Zhihong Man zman@swin.edu.au