Abstract
One of the objectives of intelligent data engineering and automated learning is to develop algorithms that learn the environment, generate rules, and take possible courses of actions. In this paper, we report our work on how to generate and apply such rules with a rule matrix model. Since the environments can be interval valued and rules often fuzzy, we further study how to obtain and apply rules for interval valued fuzzy observations.
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References
Berleant, D., et al.: Dependable Handling of Uncertainty. Reliable Computing 9, 407–418 (2003)
de Korvin, A., Hu, C., Sirisaengtaksin, O.: On Firing Rules of Fuzzy Sets of Type II. J. Applied Mathematics 3(2), 151–159 (2000)
Kearfott, B., Dawande, M., Du, K., Hu, C.: Algorithm 737: INTLIB: a Portable Fortran-77 Interval Standard Function Library. ACM, Trans. on Math. Software 20(4), 447–459 (1994)
Mendel, J.M.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice-Hall, Englewood Cliffs (2000)
Moore, R.: Methods and Applications of Interval Analysis. Society for Industrial and Applied Mathematics (1979)
Pawlak, Z.: Rough Sets and Fuzzy Sets and Systems, pp. 99–102 (1985)
Pedrycz, W., Gomide, F.: An Introduction To Fuzzy Sets Analysis and Design. MIT Press, Cambridge (1998)
Rasiowa, H.: Towards Fuzzy Logic Infuzzy Logic for the Management of Uncertainty. In: Zadh, L., Kacprzyk, J. (eds.), pp. 121–139. Wiley Interscience, New York
Roger, J.S.: ANFIS: Adaptive Network-based Fuzzy Inference System. IEEE Trans. on Systems, Man and Cybernetics 23(03), 665–685 (1993)
Shary, S.: A New Technique in Systems Analysis Under Interval Uncertainty and Ambiguity. Reliable Computing 8, 321–418 (2002)
Zadeh, A.L.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. on Systems, Man and Cybernetics 3(1), 28–44 (1973)
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de Korvin, A., Hu, C., Chen, P. (2004). Generating and Applying Rules for Interval Valued Fuzzy Observations. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_41
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DOI: https://doi.org/10.1007/978-3-540-28651-6_41
Publisher Name: Springer, Berlin, Heidelberg
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