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Reliable parameter estimation for dynamic systems with big data

Project Member(s): Su, S.

Funding or Partner Organisation: Commonwealth Scientific and Industrial Research Organisation (Commonwealth Scientific & Industrial Research Organisation)

Start year: 2015

Summary: This project will investigate the optimal design of suitable input signals to stimulate big dynamic system so that the information about the dynamic system can be extracted from the experiments. For the identification of a static model of an inertial sensor, a well selected/designed set of experimental observations with desired properties, in terms of Experimental Design (DoE), can significantly improve the accuracy of system modelling and parameter estimation. Classical static system can be formulated as a static linear parameter identification problem, for which DoE theory has been well established. However, the models associated with the big dynamic system are often nonlinear. This makes the linear approaches, which are effective and theoretically rigorous in traditional research, invalid for big system. This Project will focus on new machine learning based approaches to solve the above problem. New experimental design methods will be proposed to improve the accuracy of parameter estimation. Two performance indices of optimal experimental design, D-optimality and G-optimality, are going to be investigated based on the analysis of the information matrix.


Liu, T, Zhang, W, McLean, P, Ueland, M, Forbes, SL & Su, SW 2018, 'Electronic Nose-Based Odor Classification using Genetic Algorithms and Fuzzy Support Vector Machines', International Journal of Fuzzy Systems, vol. 20, no. 4, pp. 1309-1320.
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Yu, H, Ye, L, Naik, GR, Song, R, Nguyen, HT & Su, S 2018, 'Nonparametric Dynamical Model of Cardiorespiratory Responses at the Onset and Offset of Treadmill Exercises', Medical and Biological Engineering and Computing, vol. 56, no. 12, pp. 2337-2351.
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Ye, L, Argha, A, Celler, BG, Nguyen, HT & Su, SW 2017, 'Online auto-calibration of triaxial accelerometer with time-variant model structures', Sensors and Actuators A: Physical, vol. 266, pp. 294-307.
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Rana, M, Li, L & Su, SW 2016, 'Microgrid State Estimation Using the IoT with 5G Technology' in Mavromoustakis, CX, Mastorakis, G & Batalla, JM (eds), Internet of Things (IoT) in 5G Mobile Technologies, Springer, Germany, pp. 175-196.

Rana, M, Li, LI & Su, SW 2016, 'Distributed Condition Monitoring of Renewable Microgrids Using Adaptive-then-Combine Algorithm', IEEE Power and Energy Society General Meeting, IEEE Power and Energy Society General Meeting, IEEE, USA.
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Zhang, T, Su, S & Nguyen, HT 2016, 'The Hybrid Bio-Inspired Aerial Vehicle: Concept and SIMSCAPE Flight Simulation', Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2016 (EMBC), International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Orlando, Florida, USA, pp. 2107-2110.
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Yuwono, M, Guo, Y, Wall, J, Li, J, West, S, Platt, G & Su, SW 2015, 'Unsupervised feature selection using swarm intelligence and consensus clustering for automatic fault detection and diagnosis in Heating Ventilation and Air Conditioning systems', APPLIED SOFT COMPUTING, vol. 34, pp. 402-425.
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Alqudah, HM, Cui, X, Ye, L, Cao, K, Szymanski, J, Guo, Y & Su, S 2015, 'Modeling of Tri-axial Accelerometers in a Selfdesigned Wearable Inertial Measurement Unit', 2015 9th International Conference on Sensing Technology (ICST), International Conference on Sensing Technology, IEEE, Auckland, pp. 605-610.
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Keywords: Parameter estimation, Modeling, Experimental Design (DoE), Big Data Analysis

FOR Codes: Stochastic Analysis and Modelling, Simulation and Modelling, Expanding Knowledge in Engineering