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Prediction of output response probability of sound environment system using simplified model with stochastic regression and fuzzy inference

Author

Listed:
  • A. Ikuta

    (Prefectural University of Hiroshima)

  • N. H. Siddique

    (University of Ulster)

  • H. Orimoto

    (Prefectural University of Hiroshima)

Abstract

The traditional standard stochastic system models, such as the autoregressive (AR), moving average (MA) and autoregressive moving average (ARMA) models, usually assume the Gaussian property for the fluctuation distribution, and the well-known least squares method is applied on the basis of only the linear correlation data. In the actual sound environment system, the stochastic process exhibits various non-Gaussian distributions, and there exist potentially various nonlinear correlations in addition to the linear correlation between input and output time series. Consequently, the system input and output relationship in the actual phenomenon cannot be represented by a simple model. In this study, a prediction method of output response probability for sound environment systems is derived by introducing a correction method based on the stochastic regression and fuzzy inference for simplified standard system models. The proposed method is applied to the actual data in a sound environment system, and the practical usefulness is verified.

Suggested Citation

  • A. Ikuta & N. H. Siddique & H. Orimoto, 2013. "Prediction of output response probability of sound environment system using simplified model with stochastic regression and fuzzy inference," Fuzzy Information and Engineering, Springer, vol. 5(2), pages 173-190, June.
    • Handle: RePEc:spr:fuzinf:v:5:y:2013:i:2:d:10.1007_s12543-013-0142-4
    DOI: 10.1007/s12543-013-0142-4
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