Prof. Hua-Liang Wei
Department of Automatic Control and Systems Engineering, The University of Sheffield
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Academic Career Length
24Years (2003-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications18 July 2005 in IEEE Transactions on Neural Networks
Journal Article
SBS.A. Billings
S.A. BillingsHua-Liang WeiA new class of wavelet networks (WNs) is proposed for nonlinear system identification. In the new networks, the model structure for a high-dimensional system is chosen to be a superimposition of a number of functions with fewer variables. By expanding each function using truncated wavelet decompositions, the multivariate nonlinear networks can be converted into linear-in-the-parameter regressions, which can be solved using least-squares type methods. An efficient model term selection approach based upon a forward orthogonal least squares (OLS) algorithm and the error reduction ratio (ERR) is applied to solve the linear-in-the-parameters problem in the present study. The main advantage of the new WN is that it exploits the attractive features of multiscale wavelet decompositions and the capability of traditional neural networks. By adopting the analysis of variance (ANOVA) expansion, WNs can now handle nonlinear identification problems in high dimensions.
20 October 2021 in Human Brain Mapping
Journal Article
JCJUN CAO
JUN CAO
Yifan ZhaoXSXiaocai Shan
Xiaocai ShanHua-Liang Wei
Yuzhu GuoPSPtolemaios G Sarrigiannis
Ptolemaios G Sarrigiannis +2
Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG‐based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools. This article reviews EEG‐based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric or nonparametric, time‐based, and frequency‐based or time‐frequency‐based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics, and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented.
2 February 2018 in IEEE Transactions on Power Electronics
Journal Article
ZLZhao-Hua Liu
Zhao-Hua LiuHua-Liang WeiXLXiao-Hua Li
Xiao-Hua Li
Kan LiuQZQing-Chang Zhong
Qing-Chang ZhongA global parameter estimation method for a permanent magnet synchronous machines (PMSM) drive system is proposed, where the electrical parameters, mechanical parameters, and voltage-source-inverter (VSI) nonlinearity are regarded as a whole and parameter estimation is formulated as a single parameter optimization model. A dynamic learning estimator is proposed for tracking the electrical parameters, mechanical parameters, and VSI of PMSM drive by using dynamic self-learning particle swarm optimization (DSLPSO). In DSLPSO, a novel movement modification equation with dynamic exemplar learning strategy is designed to ensure its diversity and achieve a reasonable tradeoff between the exploitation and exploration during the search process. Moreover, a nonlinear multiscale based interactive learning operator is introduced for accelerating the convergence speed of the Pbest particles; meanwhile a dynamic opposition-based learning strategy is designed to facilitate the gBest particle to explore a potentially better region. The proposed algorithm is applied to parameter estimation for a PMSM drive system. The results show that the proposed method has better performance in tracking the variation of electrical parameters, and estimating the immeasurable mechanical parameters and the VSI disturbance voltage simultaneously.
30 November 2006 in IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal Article
Hua-Liang WeiSBStephen A. Billings
Stephen A. Billings
A new unsupervised forward orthogonal search (FOS) algorithm is introduced for feature selection and ranking. In the new algorithm, features are selected in a stepwise way, one at a time, by estimating the capability of each specified candidate feature subset to represent the overall features in the measurement space. A squared correlation function is employed as the criterion to measure the dependency between features and this makes the new algorithm easy to implement. The forward orthogonalization strategy, which combines good effectiveness with high efficiency, enables the new algorithm to produce efficient feature subsets with a clear physical interpretation
19 August 2019 in IEEE Transactions on Systems, Man, and Cybernetics: Systems
Journal Article
ZLZhao-Hua Liu
Zhao-Hua LiuBLBi-Liang Lu
Bi-Liang Lu
Hua-Liang Wei
Lei ChenXLXiao-Hua Li
Xiao-Hua LiMRMatthias Ratsch
Matthias Ratsch
Fault diagnosis of rolling bearings is an essential process for improving the reliability and safety of the rotating machinery. It is always a major challenge to ensure fault diagnosis accuracy in particular under severe working conditions. In this article, a deep adversarial domain adaptation (DADA) model is proposed for rolling bearing fault diagnosis. This model constructs an adversarial adaptation network to solve the commonly encountered problem in numerous real applications: the source domain and the target domain are inconsistent in their distribution. First, a deep stack autoencoder (DSAE) is combined with representative feature learning for dimensionality reduction, and such a combination provides an unsupervised learning method to effectively acquire fault features. Meanwhile, domain adaptation and recognition classification are implemented using a Softmax classifier to augment classification accuracy. Second, the effects of the number of hidden layers in the stack autoencoder network, the number of neurons in each hidden layer, and the hyperparameters of the proposed fault diagnosis algorithm are analyzed. Third, comprehensive analysis is performed on real data to validate the performance of the proposed method; the experimental results demonstrate that the new method outperforms the existing machine learning and deep learning methods, in terms of classification accuracy and generalization ability.
10 January 2004 in International Journal of Control
Journal Article
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 under study prior to model term detection. It is required that the selected significant variables alone should sufficiently represent the system. Generally, not all the model terms, which are produced by combining different variables, make an equal contribution to the system output and terms, which make little contribution, can be omitted. A parsimonious representation, which contains only the significant terms, can often be obtained without the loss of representational accuracy by eliminating the redundant terms. Based on these observations, a new variable and term selection algorithm is proposed in this paper. The term detection algorithm can be applied to the general class of non-linear modelling problems which can be expressed as a linear-in-the-parameters form. The variable selection procedure is based on locally linear and cross-bilinear models, which are used together with the forward orthogonal least squares (OLS) and error reduction ratio (ERR) approach to determine the significant terms and to pre-select the important variables for both time series and input–output systems. Several numerical examples are provided to illustrate the applicability and effectiveness of the new approach.
About Me
Hua-Liang Wei received his Ph.D. from the Department of Automatic Control and Systems Engineering (ACSE), University of Sheffield, UK, in 2004. Dr Wei is currently a Senior Lecturer (Associate Professor) with ACSE at the University of Sheffield. He is head of Digital Medicine and Computational Neuroscience (DMCN), head of the laboratory of Dynamic Modelling, and head of the laboratory of Data Mining and Decision Making (3DM). He has been awarded grants (as PI and Co-I) by a variety of funding bodies, including the EPSRC, NERC, STFC, EU H2020, the Royal Society, and so forth. He has published more than 160 peer-reviewed papers. His main research interests are signal processing, nonlinear system identification, interpretable machine learning and explainable AI, data-driven modelling, knowledge-informed predictive modelling, and intelligent fault detection and diagnosis. He has been devoted to these areas for over 20 years, tackling challenging issues at the interface of data science, computer science, and control and systems engineering, to develop new fundamental methods that can be adapted to solve challenging, complex systems modelling problems in multi- and inter-disciplinary areas, including EEG signal processing, medicine and bio-medicine, weather and climate, space weather, neurophysiology, neuroscience, and healthcare. He has a strong interest in initializing and developing new research directions that can help solve emerging challenging problems.