Abstract
This chapter presents a review of recently developed fuzzy-neuro models for local and personalised modelling and illustrates them on a real world case study from medical decision support. The local models are based on the principles of evolving connectionist systems, where the data is clustered and for each cluster a separate local model is developed and represented as a fuzzy rule, either of Takagi-Sugeno, or Zadeh-Mamdani types. The personalised modelling techniques are based on transductive reasoning and include a model called TWNFI. It is also illustrated on medical decision support problem where a model for each patient is developed to predict an outcome for this patient and to rank the importance of the clinical variables for them. The local and personalised models are compared with statistical, neural network and fuzzy-neuro global models and show a significant advantage in accuracy and explanation.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Albus JS (1975) A new approach to manipulator control: The cerebellar model articulation controller (CMAC), Trans. of the ASME: Journal of Dynamic Systems, Measurement, and Control 220–227
Amari S, Kasabov N (1998) Brain-like Computing and Intelligent Information Systems. Springer Verlag, NewYork
Arbib M. (1995) The .andbook of Brain Theory and Neural Networks. The MIT Press, Cambridge, MA, U.S.A.
Biehl M, Freking A, Holzer M, Reents G, Schlosser E (1998) On-line learning of Prototypes and Principal Components. Sadd D (eds) On-line learning in Neural Networks, Cambridge University Press, Cambridge, UK, pp 231–249
Bishop C (1995) Neural networks for pattern recognition. Oxford University Press, Oxford, UK
Blanzieri E, Katenkamp P (1996) Learning radial basis function networks on-line In: Morgan Kaufmann (eds) Proc. of Intern. Conf. On Machine Learning, Bari , Italy, pp. 37–45
Carpenter G, Grossberg S (1991) Pattern recognition by self-organizing neural networks. Massachusetts. The MIT Press, Cambridge, MA, U.S.A.
Chakraborty S, Pal K, Pal NR (2002) A neuro-fuzzy framework for inferencing. Neural Networks 15: 247–261
Freeman J, Saad D (1997) On-line learning in radial basis function networks. Neural Computation 9(7)
Fukuda T, Komata Y, Arakawa T (1997) Recurrent Neural Networks with Self-Adaptive GAs for Biped Locomotion Robot In: Proceedings of the International Conference on Neural Networks ICNN’97, IEEE Press
Furlanello C, Giuliani D, Trentin E (1995) Connectionist speaker normalisation with generalised resource allocation network. In: Toretzky D, Tesauro G, Lean T (eds) Advances in NIPS7 MIT Press, Cambridge, MA, U.S.A., 1704–1707
Jang R (1993) ANFIS: adaptive network based fuzzy inference system. IEEE Trans. on Syst. Man, and Cybernetics 23(3) 665–685
Kasabov N (1998) A framework for intelligent conscious machines utilising fuzzy neural networks and spatial temporal maps and a case study of multilingual speech recognition. In: Amari S, Kasabov N (eds) Brain-like computing and intelligent information systems. Springer Verlag, Singapore, pp 106–126
Kasabov N (1998) ECOS: A framework for evolving connectionist systems and the ECO learning paradigm. In: S.Usui and T.Omori (eds) Proc. of ICONIP’98, IOS Press, Kitakyushu, Japan, pp 1222–1235
Kasabov N (2002) Evolving connectionist systems: Methods and Applications in Bioinformatics, Brain study and intelligent machines. Springer Verlag, London, New York, Heidelberg
Kasabov N (1998) Evolving Fuzzy Neural Networks - Algorithms, Applications and Biological Motivation. In: Yamakawa, T. and G.Matsumoto (eds) Methodologies for the conception, design and application of soft computing, World Scientific, pp 271–274
Kasabov N (2001) Evolving fuzzy neural networks for on-line supervised/unsupervised, knowledge–based learning. IEEE Trans. SMC – part B, Cybernetics 31(6): 902–918
Kasabov N (1996) Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering. The MIT Press, CA, MA
Kasabo N, Kim JS, Watts M, Gray A (1997) FuNN/2- A Fuzzy Neural Network Architecture for Adaptive Learning and Knowledge Acquisition. Information Sciences - Applications 101(3-4): 155–175
Kasabov N, Kozma R, Kilgour R, Laws M, Taylor J, Watts M, Gray A (1999) A Methodology for Speech Data Analysis and a Framework for Adaptive Speech Recognition Using Fuzzy Neural Networks and Self Organising Maps. In: Kasabov N, Kozma (eds) Neuro-fuzzy techniques for intelligent information systems, Physica, Springer Verlag
Kasabov N, Song Q (2002) DENFIS: Dynamic, evolving neural-fuzzy inference systems and its application for time-series prediction. IEEE Trans. on Fuzzy Systems 10:144–154
Kasabov N, Woodford B (1999) Rule Insertion and Rule Extraction from Evolving Fuzzy Neural Networks: Algorithms and Applications for Building Adaptive, Intelligent Expert Systems. In: Proc. of IEEE Intern. Fuzzy Systems Conference, Seoul, Korea, pp. 1406–1411
Levey AS, Bosch, JP, Lewis JB, Greene T, Rogers N, Roth D for the Modification of Diet in Renal Disease Study Group (1999) A More Accurate Method To Estimate Glomerular Filtration Rate from Serum Creatinine: A New Prediction Equation. Annals of Internal Medicine 130: 461–470
Lin CT, Lee CSG (1996) Neuro Fuzzy Systems. Prentice Hall.
Massaro D, Cohen M (1983) Integration of visual and auditory information in speech perception Journal of Experimental Psychology: Human Perception and Performance 9: 753–771
Marshall MR, Song Q, Ma TM, MacDonell S, Kasabov N (2005) Evolving Connectionist System versus Algebraic Formulae for Prediction of Renal Function from Serum Creatinine. Kidney International 67:1944–1954
Moody J Darken C (1988) Learning with localized receptive fields. In: Touretzky D, Hinton G, Sejnowski T (eds) Proceedings of the 1988 Connectionist Models Summer School, San Mateo, Carnegie Mellon University, Morgan Kaufmann
Moody J, Darken C (1989) Fast learning in networks of locally-tuned processing units Neural Computation 1: 281–294
Neural Network Toolbox User’s Guide (2001). The Math Works Inc., ver. 4.
Platt, J (1991) A resource allocating network for function interpolation. Neural Computation 3: 213–225
Poggio T (1994) Regularization theory, radial basis functions and networks. In: From Statistics to Neural Networks: Theory and Pattern Recognition Applications. NATO ASI Series, No.136, pp. 83–104
Rummery GA, Niranjan M (1994) On-line Q-learning using connectionist system. Cambridge University Engineering Department, CUED/F-INENG/TR , pp166
Schaal S, Atkeson C (1998) Constructive incremental learning from only local information. Neural Computation 10: 2047–2084
Song Q, Kasabov N (2003) Weighted Data Normalizations and feature Selection for Evolving Connectionist Systems Proceedings. In: Lovell BC, Campbell DA, Fookes CB, Maeder AJ (eds) Proc. of The Eighth Australian and New Zealand Intelligence Information Systems Conference (ANZIIS2003), Sydney, Australia, pp. 285–290
Song, Q, Kasabov N (2001) ECM - A Novel On-line, Evolving Clustering Method and Its Applications. In: Kasabov N, Woodford BJ (eds) Proceedings of the Fifth Biannual Conference on Artificial Neural Networks and Expert Systems (ANNES2001), Dunedin, New Zealand, pp. 87–92
Song Q, Kasabov N (2004) TWNFI – Transductive Neural-Fuzzy Inference System with Weighted Data Normalization and Its Application in Medicine. IEEE Neural Networks. (accepted)
Song Q, Ma TM, Kasabov N (2003) A Novel Generic Higher-order TSK Fuzzy Model for Prediction and Applications for Medical Decision Support. In: Lovell BC, Campbell DA, Fookes CB, Maeder AJ (eds) Proc. of The Eighth Australian and New Zealand Intelligence Information Systems Conference (ANZIIS2003), Sydney, Australia, pp. 241–245
Song Q, Ma TM, Kasabov N (2004) LR-KFNN: Logistic Regression-Kernel Function Neural networks and the GFR-NN Model for Renal Function Evaluatio. In: Mohammadian M (eds) Proc. Of International Conference on Computational Intelligence for modelling, Control and Automation 2004 (CIMCA2004), Gold Coast, Australia, pp. 946–951
Song Q, Ma TM, Kasabov N (2005) Transductive Knowledge Based Fuzzy Inference System for Personalized Modeling. Lecture Notes of Artificial Intelligence 3614: 528–535
Takagi T, Sugeno M (1985) Fuzzy Identification of systems and its applications to modeling and control. IEEE Trans. on Systems, Man, and Cybernetics 15: 116–132
Vapnik V(1998) Statistical Learning Theory. John Wiley & Sons Inc.
Wang LX (1994) Adaptive Fuzzy System And Control: Design and Stability Analysis. Englewood Cliffs, NJ: Prentice Hall
Woldrige M, Jennings N (1995) Intelligent agents: Theory and practice. The Knowledge Engineering review (10).
Zadeh LA (1988) Fuzzy Logic. IEEE Computer 21: 83–93
Zadeh LA (1965) Fuzzy Sets. Information and Control 8: 338–353
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Kasabov, N., Song, Q., Ma, T.M. (2008). Fuzzy-Neuro Systems for Local and Personalized Modelling. In: Forging New Frontiers: Fuzzy Pioneers II. Studies in Fuzziness and Soft Computing, vol 218. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73185-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-540-73185-6_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73184-9
Online ISBN: 978-3-540-73185-6
eBook Packages: EngineeringEngineering (R0)