A practical probabilistic evaluation method for actually complicated insulation systems based on linear and/or non-linear regression models between observed input and output data of the system.

Abstract

Until now, the output probability distribution prediction for the sound insulation system was investigated by many researchers based on two contrastive structural and descriptive approaches especially from a methodological viewpoint. The descriptive method is certainly more suitable for the complicated stituation of actual sound insulation systems with an arbitrary distribution type random excitation. In this paper, especially by paying our attention to the effective information on the remainder fluctuation around the formally introduced linear regression, a simplified identification method for an arbitrary sound insulation system and then a simple prediction method for its output probability distribution have been proposed. More concretely, by introducing new types of linear and/or non-linear regression models between input and output fluctuations, their system parameters can be identified by use of the well-known least squares error method or its generalized error criterion. Furthermore, in an actual case with an existence of background noise, the effect and simple removal countermeasures of background noise are also discussed. The effectiveness of our identification and prediction methods is experimentally confirmed too by applying them to the actual living environment.