Extreme Learning Machines for Pattern Classification and System Modelling

Extreme Learning Machine (ELM), as an effective training methodology for feed-forward neural networks (FNN), has been widely used to perform the universal approximation and parallel processing in science and engineering. In recent years, ELM’s applications have spread out from manufacturing, transportation, process control, dynamic system modelling, digital signal and image processing to information retrieval with vast amount of data. When a single hidden layered feed-forward neural network (SLFNN) is trained with ELM, the input weights are uniformly randomly selected in a range, and the output weights are then optimally designed by using the batch learning type of least squares or other regularization methods. In such a way, the input data are mapped to the feature space at the outputs of the hidden layer. In particular, when the number of the hidden nodes is greater than the number of input patterns, the data features are sparsely distributed in the feature space. This remarkable characteristic of SLFNNs trained with ELM makes it possible to linearly separate the features of the input data in the feature space, which may be nonlinearly separable in the input space. In addition, many results in signal/image processing and big data analysis have shown that the SLFNNs trained with ELM have excellent capabilities of interpolation, universal approximation and generalization, and has actually filled the gap between machine learning and biological learning. More recently, ELM has combined with deep-learning, Bayesian belief and fuzzy logic to exhibit a powerful role as biological learning in the area of artificial intelligence.

Authors are invited to submit their original research manuscripts with new findings in ELM theories and applications to pattern classification, system modelling and big data analysis to this special issue.

Topics:

The topics of interest are:

• Clustering, feature extraction and pattern classification

• Combination of deep learning and ELM

• Combination of Bayesian belief and ELM

• Financial modelling and data analysis

• Engineering system modelling

• Human computer interface and brain computer interface

• Cognitive computation

• Big data analytics

Submission guidelines:

Research articles must not be published or submitted for publication elsewhere. All articles will be peer reviewed and accepted based on quality, originality, novelty, and relevance to the special issue theme. Before submission, authors should carefully read over the journal\’s Author Guidelines, which is available at

http://www.elsevier.com/wps/find/journaldescription.cws_home/367/authorinstructions

Papers must be submitted online at: https://www.evise.com/profile/#/COMPELECENG/login

by selecting “SI-elm” from the “Issues” pull-down menu during the submission process.

Schedule:

Submission of manuscript: April 15, 2019

First notification: June 15, 2019

Submission of revised manuscript: August 15, 2019

Notification of the re-review: September 15, 2016

Final notification: November 15, 2019

Final paper due: January 15, 2020

Publication date: May 2020

Guest Editors:

Zhihong Man, PhD (Managing Guest Editor)

Faculty of Science, Engineering and Technology

Swinburne University of Technology

Victoria 3122, Australia

Email: zman@swin.edu.au

Jonathan Wu, PhD

Faculty of Engineering

University of Windsor

2285 Wyandotte St. W

Windsor, Ont. Canada

Email: jwu@uwindsor.ca

Guest Editors\’ short bios:

Zhihong Man received his BE degree from Shanghai Jiaotong University China in 1982, his MSc degree from Chinese Academy of Sciences in 1987 and his PhD degree from The University of Melbourne Australia in 1994, respectively. He was the Lecturer and then Senior Lecturer of Electrical Engineering at The University of Tasmania from 1996 to 2001, the Associate Professor of Computer Engineering at Nanyang Technological University from 2002 to 2007 and the Professor of Electrical and Computer Systems Engineering at Monash University Sunway Campus from 2007 to 2008. Since 2009, Zhihong has been with Swinburne University of Technology Australia as the Professor of Robotics and Mechatronics.

Zhihong’s research interests are in variable structure systems, robot control systems, neural networks, signal processing, complex system modelling, vehicle dynamics & control as well as the fault modelling and diagnosis of aircraft engines. he has published more than 250 refereed journal and conference papers and authored two books on robotics and signals & systems. Zhihong has actively worked with IEEE Industrial Electronics society as the general chair, program chair and program committee member of many international conferences. He has been guest editor of seven journal special issues and has presented keynotes and talks at many international conferences and universities since 2000.

Jonathan Wu received his PhD degree from the University of Wales, U.K. in 1990. He has been affiliated with the National Research Council of Canada for ten years since 1995, where he became a senior research officer and a group leader. He is currently a professor in the Department of Electrical and Computer Engineering at University of Windsor, Windsor, ON, Canada. He has authored over 350 peer-reviewed papers in computer vision, image processing, , intelligent systems,robotics, interactive microsystems. His current research interests include machine learning, 3D computer vision, interactive multimedia, human-machine interaction, sensor analysis and fusion and autonomous robotic systems.

Dr Wu holds the Tier 1 Canada Research Chair in automotive sensors and information systems. He has served on technical program committees and international advisory committees for many prestigious conferences. He was an associate editor of IEEE Trans. on Systems, Man, and Cybernetics Part A, IEEE Trans. on Neural Networks and Learning Systems and International Journal of Robotics and Automation. He is currently an associate editor of IEEE Trans. on Circuit and Systems for Video Technology and IEEE Trans. on Cybernetics.