Abstract
For better solving some complicated problems in fuzzy automata hierarchy, simultaneously, in order to accomplish better task for fuzzy signal processing, this paper presents a kind of new automaton–fuzzy infinite-state automaton. The basic extracted frame of fuzzy infinite-state automaton is introduced by using neural networks. To the extracted fuzzy infinite-state automaton, this paper describes that it is equivalent to fuzzy finite-state automaton, and its convergence and stability on its hierarchy system will be discussed. Finally, the simulation is carried on and the simulation results show that the states of fuzzy infinite-state automaton converge to some stable states with extraction frame and training for weights what this paper provides at last. Finally, some problems of fuzzy infinite-state automaton and neural networks to be solved and development trends are discussed. These researches will not only extend further automata hierarchy, but also increase a new tool for application of fuzzy signal processing. It is an important base in the application of fuzzy automata theory.
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References
Blanco, A., Delgado, M., Pegalajar, M.C.: Identification of fuzzy dynamic systems using max-min recurrent neural networks. Fuzzy Set and Systems 1, 63–70 (2000)
Kosmatopoulos, E.B., Christodoulou, M.A.: Neural networks for identification of fuzzy dynamical systems: An Application to identification of vehicle highway systems. In: Proc. 4th IEEE Mediterranean Symp. New Directions in control and Automation, vol. 1, pp. 23–38 (1996)
Kosmatopoulos, E.B., Polycarpou, M.M., Christodoulou, M.A., et al.: High-order neural networks for identification of dynamical systems. IEEE Trans. Neural networks 6, 422–431 (1995)
Kosmatopoulos, E.B., Christodoulou, M.A.: Recurrent neural networks for approximation of fuzzy dynamical systems. Int. J. Intell. Control Syst. 1, 223–233 (1996)
Omlin, C.W., Thornber, K.K., Giles, C.L.: Fuzzy finite state automata can be deterministically encoded into recurrent neural networks. IEEE Trans. Fuzzy Syst. 6, 76–89 (1998)
Giles, C.L., Miller, C.B., Chen, D., et al.: Learning and extracting finite state automata with second-order recurrent neural networks. Neural Computation 4, 393–405 (1992)
Lalande, A., Jaulent, M.: A fuzzy automaton to detect and quantify fuzzy artery lesions from arteriograms. In: Proceedings of the Sixth International Conference IPMU 1996, vol. 3, pp. 1481–1487 (1996) (in Canada)
Radim, B.: Determinism and fuzzy automata. Information Sciences 143, 205–209 (2002)
Naiyao, Z., Pingfan, Y.: Neural Networks and Fuzzy Control. Tsing-Hua University Press (1998) (Chinese)
Jiangwen, Y., Heng, S., Longsi, G.: Neural network and fuzzy signal processing. Science Press (2003) (Chinese)
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Wu, Q., Wang, T., Huang, Y., Li, J. (2006). Theory Research on a New Type Fuzzy Automaton. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_1
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DOI: https://doi.org/10.1007/11881599_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45916-3
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