Parameter Magnitude-Based Information Criterion For Optimum Model Structure Selection In System Identification

Model structure selection is among one of the steps in system identification and in order to carry out this, information criterion is developed. It plays an important role in determining an optimum model structure with the aim of selecting an adequate model to represent a real system. A good informa...

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Main Author:	Mohd Nasir, Abdul Rahman
Format:	Thesis
Language:	English English
Published:	2020
Subjects:	Q Science (General) QA Mathematics
Online Access:	http://eprints.utem.edu.my/id/eprint/25448/1/Parameter%20Magnitude-Based%20Information%20Criterion%20For%20Optimum%20Model%20Structure%20Selection%20In%20System%20Identification.pdf http://eprints.utem.edu.my/id/eprint/25448/2/Parameter%20Magnitude-Based%20Information%20Criterion%20For%20Optimum%20Model%20Structure%20Selection%20In%20System%20Identification.pdf
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id	my-utem-ep.25448
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Abd Samad, Md Fahmi
topic	Q Science (General) QA Mathematics
spellingShingle	Q Science (General) QA Mathematics Mohd Nasir, Abdul Rahman Parameter Magnitude-Based Information Criterion For Optimum Model Structure Selection In System Identification
description	Model structure selection is among one of the steps in system identification and in order to carry out this, information criterion is developed. It plays an important role in determining an optimum model structure with the aim of selecting an adequate model to represent a real system. A good information criterion not only evaluates predictive accuracy but also the parsimony of model. There are many information criteria those are widely used such as Akaike information criterion (AIC) and Bayesian information criterion (BIC). On bias evaluation, these criteria only tackle on the number of parameters in a model. There scarcely have been any information criterion that evaluates parsimony of model structures (bias contribution) based on the magnitude of parameter or coefficient. The magnitude of parameter could have a big role in choosing whether a term is significant enough to be included in a model and justifies one’s judgement in choosing or discarding a term/variable. This study presents the comparison between parameter-magnitude based information criterion 2 (PMIC2), PMIC (an earlier version of its kind), AIC and BIC in selecting a correct model on simulated data and real data. For simulated data, PMIC2 was compared to AIC and BIC using enumerative approach and genetic algorithm. The test were made to a number of simulated systems in the form of discrete-time models of various linearity, lag orders and number of terms/variables. Then, PMIC2 was tested in selecting a good model to represent a real system based on gas furnace data and the results is compared to PMIC. The selected model was then tested using correlation test for model validation. Overall conclusion, it is shown that PMIC2 is able to select a more parsimonious model, yet adequately accurate, than AIC, BIC and PMIC.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Mohd Nasir, Abdul Rahman
author_facet	Mohd Nasir, Abdul Rahman
author_sort	Mohd Nasir, Abdul Rahman
title	Parameter Magnitude-Based Information Criterion For Optimum Model Structure Selection In System Identification
title_short	Parameter Magnitude-Based Information Criterion For Optimum Model Structure Selection In System Identification
title_full	Parameter Magnitude-Based Information Criterion For Optimum Model Structure Selection In System Identification
title_fullStr	Parameter Magnitude-Based Information Criterion For Optimum Model Structure Selection In System Identification
title_full_unstemmed	Parameter Magnitude-Based Information Criterion For Optimum Model Structure Selection In System Identification
title_sort	parameter magnitude-based information criterion for optimum model structure selection in system identification
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty Of Mechanical Engineering
publishDate	2020
url	http://eprints.utem.edu.my/id/eprint/25448/1/Parameter%20Magnitude-Based%20Information%20Criterion%20For%20Optimum%20Model%20Structure%20Selection%20In%20System%20Identification.pdf http://eprints.utem.edu.my/id/eprint/25448/2/Parameter%20Magnitude-Based%20Information%20Criterion%20For%20Optimum%20Model%20Structure%20Selection%20In%20System%20Identification.pdf
_version_	1747834130792448000
spelling	my-utem-ep.254482021-12-12T22:18:02Z Parameter Magnitude-Based Information Criterion For Optimum Model Structure Selection In System Identification 2020 Mohd Nasir, Abdul Rahman Q Science (General) QA Mathematics Model structure selection is among one of the steps in system identification and in order to carry out this, information criterion is developed. It plays an important role in determining an optimum model structure with the aim of selecting an adequate model to represent a real system. A good information criterion not only evaluates predictive accuracy but also the parsimony of model. There are many information criteria those are widely used such as Akaike information criterion (AIC) and Bayesian information criterion (BIC). On bias evaluation, these criteria only tackle on the number of parameters in a model. There scarcely have been any information criterion that evaluates parsimony of model structures (bias contribution) based on the magnitude of parameter or coefficient. The magnitude of parameter could have a big role in choosing whether a term is significant enough to be included in a model and justifies one’s judgement in choosing or discarding a term/variable. This study presents the comparison between parameter-magnitude based information criterion 2 (PMIC2), PMIC (an earlier version of its kind), AIC and BIC in selecting a correct model on simulated data and real data. For simulated data, PMIC2 was compared to AIC and BIC using enumerative approach and genetic algorithm. The test were made to a number of simulated systems in the form of discrete-time models of various linearity, lag orders and number of terms/variables. Then, PMIC2 was tested in selecting a good model to represent a real system based on gas furnace data and the results is compared to PMIC. The selected model was then tested using correlation test for model validation. Overall conclusion, it is shown that PMIC2 is able to select a more parsimonious model, yet adequately accurate, than AIC, BIC and PMIC. 2020 Thesis http://eprints.utem.edu.my/id/eprint/25448/ http://eprints.utem.edu.my/id/eprint/25448/1/Parameter%20Magnitude-Based%20Information%20Criterion%20For%20Optimum%20Model%20Structure%20Selection%20In%20System%20Identification.pdf text en public http://eprints.utem.edu.my/id/eprint/25448/2/Parameter%20Magnitude-Based%20Information%20Criterion%20For%20Optimum%20Model%20Structure%20Selection%20In%20System%20Identification.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=119756 mphil masters Universiti Teknikal Malaysia Melaka Faculty Of Mechanical Engineering Abd Samad, Md Fahmi 1. Ahmad, R., Jamaluddin, H., and Hussain, M. A., 2002. Multivariable System Identification for Dynamic Discrete-Time Nonlinear System Using Genetic Algorithm. IEEE International Conference on Systems, Man and Cybernetics, Oct 6-9. Hammamet, Tunisia: IEEE, pp. 5-6. 2. Akaike, H., 1974. A New Look at The Statistical Model Identification. IEEE Transactions on Automatic Control, 19(6), pp. 716-723. 3. Akaike, H., 1972. Information Theory and An Extension of The Maximum Likelihood Principle. Proceedings 2nd International Symposium on Information Theory, Supplement to Problems of Control and Information Theory, pp. 267-281. 4. Alfi, A., and Fateh, M. M., 2010. Parameter Identification Based on a Modified PSO Applied to Suspension System. Journal of Software Engineering and Applications, 3, pp. 221-229. 5. Almpanidis, G., and Kotropoulos, C., 2008. Phonemic Segmentation using The Generalised Gamma Distribution and Small Sample Bayesian Information Criterion. Speech Communication, 50(1), pp. 38-55. 6. Almpanidis, G., Kotti, M., and Kotropoulos, C., 2009. Robust Detection of Phone Boundaries Using Model Selection Criteria with Few Observations. IEEE Transactions on Audio, Speech And Language Processing, 17(2), pp. 287-298. 7. Alves da Silva, A. P. and Abrão P. J., 2002. Applications of Evolutionary Computation in Electric Power Systems. Proceedings of the 2002 Congress on Evolutionary Computation (CEC’02). May 12-17. Honolulu, Hawaii, USA: IEEE, 2, pp. 1057-1062. 8. Aytug, H., Khouja, M. and Vergara, F. E., 2003. Use of Genetic Algorithms to Solve Production and Operations Management Problems: A Review. International Journal of Production Research. 41(17), pp. 3955-4009. 9. Bäck, T., 2002. Adaptive Business Intelligence Based on Evolution Strategies: Some Application Examples of Self-Adaptive Software. Information Sciences, 148(1- 4), pp. 113-121. 10. Bäck, T. and Fogel, D. B., 2000. Glossary. In Bäck, T, Fogel, D. B. and Michalewicz, Z. (Eds.). Evolutionary Computation 1: Basic Algorithms and Operators (pp. xxi-xxxvii). Bristol: Institute of Physics Publishing. 11. Billings, S. A., Jamaluddin H. B. and Chen S., 1991. A Comparison of the Backpropagation and Recursive Prediction Error Algorithms for Training Neural Networks. Mechanical Systems and Signal Processing. 5(3), pp. 233-255. 12. Billings, S. A. and Tao, Q. H., 1991. Model Validity Tests for Nonlinear Signal Processing Applications. International Journal of Control, 54(1), pp. 157-194. 13. Billings, S. A., and Voon, W. S. F., 1986. Correlation Based Model Validity Tests for Non-Linear Models. International Journal of Control, 44(1), pp. 235-244. 14. Billings, S. A., and Voon, W. S. F., 1983. Structure Detection and Model Validity Tests in The Identification of Nonlinear Systems. IEE Proceedings D - Control Theory and Applications, 130(4), pp. 193-199. 15. Billings, S. A., and Wei, H. L., 2005. A New Class of Wavelet Networks for Nonlinear System Identification. IEEE Transactions on Neural Networks, 16(4), pp. 862-874. 16. Billings, S. A., and Yang, Y., 2003a. Identification of the Neighborhood and CA Rules From Spatio-Temporal CA Patterns. IEEE Transactions on Systems, Man and Cybernetics – Part B; Cybernetics, 33(2), pp. 332-339. 17. Billings, S. A., and Yang, Y., 2003b. Identification of Probabilistic Cellular Automata. IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics, 33(2), pp. 225-236. 18. Billings, S. A., and Zheng, G. L., 1995. Radial Basis Function Network Configuration Using Genetic Algorithms. Neural Networks, 8(6), pp. 877-890. 19. Billings, S. A., and Zhu, Q. M., 1995. Model Validation Tests for Multivariable Nonlinear Models Including Neural Networks. International Journal of Control, 62(4), pp.749-766. 20. Billings, S. A., and Zhu Q. M., 1994. Nonlinear Model Validation Using Correlation Tests. International Journal of Control, 60(6), pp. 1107-1120. 21. Bonabeau, E., Dorigo, M., and Theraulaz, G., 1999. Swarm Intelligence: From Natural to Artificial Systems, New York: Oxford University Press. 22. Bozdogan, H., 2000. Akaike’s Information Criterion and Recent Developments in Information Complexity. Journal of mathematical psychology, 44(1), pp. 62-91. 23. Burnham, K. P., and Anderson, D. R., 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2

Parameter Magnitude-Based Information Criterion For Optimum Model Structure Selection In System Identification

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