Abstract
This paper is concerned with the modeling and identification of time series data corrupted by noise using nonsingleton fuzzy logic system (NFLS). Main characteristic of the NFLS is a fuzzy system whose inputs are modeled as fuzzy number. So the NFLS is especially useful in cases where the available training data, or the input data to the fuzzy logic system, are corrupted by noise Simulation results of the Box-Jenkin’s gas furnace data will be demonstrated to show the performance. We also compare the results of the NFLS approach with the results of using only a traditional fuzzy logic system. Thus it can be considered NFLS does a much better job of modeling noisy time series data than does a traditional fuzzy logic system.
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© 2004 Springer-Verlag Berlin Heidelberg
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Kim, D., Huh, SH., Park, GT. (2004). Modeling Corrupted Time Series Data via Nonsingleton Fuzzy Logic System. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_202
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DOI: https://doi.org/10.1007/978-3-540-30499-9_202
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