This project investigates new approaches of modelling for the non-stationary data sets, which are commonly generated in many systems including Radar, Sonar, communications, instrumentation, seismic exploration, speech processing and recognition, etc. Signal processing functions usually perform based on a pre-set model, or the system structure is fixed. Although this provides a simple solution, it is highly inefficient especially for non-stationary systems that are common in practice. This makes it highly desirable to adapt the model so that it can capture the true underlying dynamics and predicts accurately the output for unseen data.
Against this background, however, there is a lack of generic tools/ methodologies to deal with the problem as on how to perform the model structural changes as demanded by the processes. Some nonlinear model structure identification algorithms are too slow for real-time applications. Most current real-time algorithms, on the other hand, are ad hoc rather than principled approaches. Hence the resultant model quality is not optimal from a statistical point of view.
In this programme, we propose to develop a hybrid, flexible yet principled approach for optimum on-line adaptive modelling by means of minimal model structure determination and simultaneous parameter estimation. The aim of the proposal is to introduce a new technique for the adaptive modelling of complex nonlinear dynamical systems in real time and noisy environments.
Project Supervisor
Dr Gong obtained BEng and MEng at University of Electronic Science & Technology of China in 1992 and 1995 respectively, and PhD from National University of Singapore in 2002. From 2002 to 2003, he worked firstly as an engineer, and later a senior engineer, at Institute of Inforcom in Singapore. In 2003, he joined Queen’s University of Belfast as a Research fellow, and later took an engineer post at the Institute of Electronics, Communications and Information Technology (ECIT), QUB in 2005. Since 2006, Dr Gong has been with University of Reading as lecturer. His research interests include communications, signal processing, adaptive filtering etc.
