Summary
This chapter introduces a Neural-Fuzzy (NF) modelling structure for offline incremental learning. Using a hybrid model-updating algorithm (supervised/unsupervised), this NF structure has the ability to adapt in an additive way to new input–output mappings and new classes. Data granulation is utilised along with a NF structure to create a high performance yet transparent model that entails the core of the system. A model fusion approach is then employed to provide the incremental update of the system. The proposed system is tested against a multi-dimensional modelling environment consisting of a complex, non-linear and sparse database.
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References
A Bargiela and W Pedrycz (2003) Granular Computing an Introduction. Kluwer Academic Publishers
G Carpenter et al. (1991) FuzzyARTMAP: A neural network architecture for incremental supervised learning of analog multi-dimensional maps. IEEE Transactions on Neural Networks, 3, 698–713
MY Chen and DA Linkens (2001) A systematic neuro-fuzzy modelling framework with application to material property prediction. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 31(5), 781–790
WD Cheung, J Han et al. (1996) Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique. ICDE, 12th Int. Conf. on Data Engineering, IEEE Computer Society, Washington DC, USA
N Kasabov (1998) ECOS: A framework for evolving connectionist systems and the ECO learning paradigm. Proceedings of the International Conference on Neural Information Processing, Kitakyushu, Japan: IOS, pp. 1222–1235
N Kasabov (2001) Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning. IEEE Transactions on Systems Man and Cybernetics, B 31, 902–918
DA Linkens and L Vefghi (1997) Recognition of patient anaesthetic levels: neural network systems, principal components analysis, and canonical discriminant variates. Artificial Intelligence in Medicine 11(2), 155–173
NR Pal and JC Bezdek (1994) Measuring fuzzy uncertainty. IEEE Transactions on Fuzzy Systems, 2(2)
G Panoutsos and M Mahfouf (2005) Granular computing and evolutionary fuzzy modelling for mechanical properties of alloy steels. IFAC 2005, Proceedings of, 16th IFAC World Congress, July 4–8 2005, Prague, Czech Republic
G Panoutsos and M Mahfouf (2005) Discovering knowledge and modelling systems using granular computing and neurofuzzy Structures. NiSIS’05, 1st Symposium, Nature Inspired Smart Information Systems, Algarve, Portugal, 3–5 Oct. 2005
J Tenner (1999) Optimisation of the heat treatment of steel using neural networks Ph.D. Thesis, Department of Automatic Control and Systems Engineering, The University of Sheffield, UK
YY Yang, DA Linkens, and M Mahfouf (2003) Genetic algorithms and hybrid neural network modelling for aluminium stress–strain prediction. Proceedings of the Institute of Mechanical Engineers: Part I – Journal of Systems and Control Engineering, 217(I), 7–21
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Panoutsos, G., Mahfouf, M. (2008). An Incremental Learning Structure Using Granular Computing and Model Fusion with Application to Materials Processing. In: Chountas, P., Petrounias, I., Kacprzyk, J. (eds) Intelligent Techniques and Tools for Novel System Architectures. Studies in Computational Intelligence, vol 109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77623-9_8
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DOI: https://doi.org/10.1007/978-3-540-77623-9_8
Publisher Name: Springer, Berlin, Heidelberg
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