Enhanced algorithm for randomised model structure selection

Abstract

Model structure selection for nonlinear system identification has been widely studied over the last 30 years due to its great importance. There are many methods in the literature to deal with structure selection, although these methods have their specific benefits, they face some difficulties in selecting the structure for a parsimonious model. In this paper, two methods based on the Randomised Model Structure Selection (RaMSS) approach are introduced in order to deal with the structure selection problem. The first one is the Randomised Model Structure Selection with Error Reduction Ratio (RaMSS-ERR) that uses the error reduction ratio as a filter for the terms analysis, improving the convergence, and the second one is the Randomised Model Structure Selection with Genetic Inheritance (RaMSS-EGI) that uses a genetic inheritance in order to get a faster convergence. The methods were applied to benchmark models and the results are encouraging. Applications to systems with a large candidate regressor set and to a continuous stirred-tank reactor are also carried out. The results show that the proposed method may be used to identify both linear and nonlinear model structures with a reduced number of iterations, computational time, and number of explored models.

Keywords:

• Model structure selection
• NARX models
• nonlinear model
• genetic inheritance
• randomised model structure selection

Data availability statement

The data that support the findings of this study are available from the corresponding author, [L. P. Fagundes], upon reasonable request.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

L. P. Fagundes

Luis Paulo Fagundes received his B.Eng. degree in Industrial Automation Engineering from Centro Federal de Educação Tecnológica de Minas Gerais in 2012 and M. Sc. degree in electrical engineering from Federal University of Uberlândia in 2015. He currently holds a position as professor at Centro Federal de Educação Tecnológica de Minas Gerais and is a Ph.D. student at Federal University of Uberlandia. His research interests include control system, system identification, genetic algorithms, and evolving systems.

A. S. Morais

Aniel Silva de Morais received his B.Eng degree and M. Sc. degree in electrical engineering from Federal University of Uberlândia, in 2002 and 2004 respectively, and the Ph.D. degree in electrical engineering from Federal University of Santa Catarina in 2008. He is currently professor at the Federal University of Uberlândia. His research interests include control systems (continuous, discrete, MIMO, nonlinear, robust, predictive), system identification, autonomous systems, CC-CC converters, new topologies of converters, electronic power converters applied to renewable energy sources and cc microgrids.

L. C. Oliveira-Lopes

Luis Claudio Oliveira-Lopes is professor at Universidade Federal de Uberlândia (UFU), Brazil, in the School of Chemical Engineering. He earned his Ph.D. in Chemical Engineering at Lehigh University (USA), M.Sc. in Chemical Engineering at COPPE/UFRJ (Brazil), and B.S. in Chemical Engineering at the Universidade Federal da Bahia (UFBA), Brazil, he studied Petrochemical engineering at University of Bologna (Italy) and was a visiting researcher in the Chemical and Biomolecular Engineering Department at UCLA (USA). He has interest and research contributions in the areas of energy production, process control, hybrid systems, optimization, industrial safety, and fault detection and diagnosis.

J. S. Morais

Josué Silva de Morais received his B.Eng degree in electrical engineering (2003), M. Sc. degree (2010) and Ph.D. degree (2013) all from Federal University of Uberlândia. He is currently a professor at the Federal University of Uberlândia. He has experience in the areas of industrial electronics, control, and instrumentation systems, acting mainly in the following themes: advanced control, automation and industrial instrumentation, unmanned air vehicle and identification of systems.