Abstract
New potential applications for neural networks and fuzzy systems are emerging in the context of ubiquitous computing and ambient intelligence. This new paradigm demands sensitive and adaptive embedded systems able to deal with a large number of stimulus in an efficient way. This paper presents a design methodology, based on a new Matlab tool, to develop computational-efficient neuro-fuzzy systems. To fulfil this objective, we have introduced a particular class of adaptive neuro-fuzzy inference systems (ANFIS) with piecewise multilinear (PWM) behaviour. Results obtained show that the PWM-ANFIS model generates computational-efficient implementations without loss of approximation capabilities or learning performance. The tool has been used to develop both software and hardware approaches as well as special architectures for hybrid hardware/software embedded systems.
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References
Jang, J.-S.R., Sun, C.-T., Mizutani, E.: Neuro-Fuzzy and Soft Computing, Part VII. Prentice Hall, Upper Saddle River (1997)
Acampora, G., Loia., V.: A Proposal of Ubiquitous Fuzzy Computing for Ambient Intelligence. Inf. Science 178, 631–646 (2008); 5, 3005–3009 (2003)
Jang, J.S.R.: ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Systems, Man, and Cybernetics 23(3), 665–685 (1993)
Buckley, J.J.: Sugeno type controllers are universal controllers. Fuzzy Sets Systems 53(3), 299–303 (1993)
Kosko, B.: Fuzzy systems as universal approximators. IEEE Transactions on Computers 43(11), 1329–1333 (1994)
Rovatti, R.: Fuzzy piecewise multilinear and piecewise linear systems as universal approximators in Sobolev norms. IEEE Transactions on Fuzzy Systems 6(2), 235–249 (1998)
Zeng, X.-J., Singh, M.G.: Approximation accuracy analysis of fuzzy systems as function approximators. IEEE Transactions on Fuzzy Systems 4(1), 44–63 (1996)
Basterretxea, K., del Campo, I., Tarela, J.M., Bosque, G.: An Experimental Study on Nonlinear Function Computation for Neural/Fuzzy Hardware Design. IEEE Transaction on Neural Networks 18(1), 266–283 (2007)
Echanobe, J., del Campo, I., Bosque, G.: An Adaptive Neuro-Fuzzy System for Efficient Implementations. Inf. Science 178(9), 2150–2162 (2008)
del Campo, I., Echanobe, J., Bosque, G., Tarela, J.M.: Efficient Hardware/Software Implementation of an Adaptive Neuro-Fuzzy System. IEEE Transactions on Fuzzy Systems 16(3), 761–778 (2008)
Lee, S.J., Ouyang, Ch.S.: A Neuro-Fuzzy System Modeling With Self-Constructing Rule Generation and Hybrid SVD-Based Learning. IEEE Transactions on Fuzzy Systems 11(3), 341–353 (2003)
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Bosque, G., Echanobe, J., del Campo, I., Tarela, J.M. (2008). Modeling and Synthesis of Computational Efficient Adaptive Neuro-Fuzzy Systems Based on Matlab. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8_14
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DOI: https://doi.org/10.1007/978-3-540-87559-8_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87558-1
Online ISBN: 978-3-540-87559-8
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