A Computer-Aided Identification Method of Arbitrary Stochastic System Contaminated by Modelling Errors and Background Noises
Summary :
Generally speaking, in the actual situation of evaluating or predicting the stochastic random phenomena, it is sometimes inevitable to consider the undesirable modelling error generated from an incomplete situation in the actual measurement (i.e., the observations in an actual environmental field are very often given under the unsatisfactory situation without keeping the idealized physical property assumed abstractively in the theoretical research), together with the inevitable additional background noise of arbitrary distribution type. Thus, the unified statistical treatment for the resultant data contaminated by these additional noises of different type is newly proposed in this paper from the system-theoretical viewpoint closely related to environmental field, based on Bayes' theorem and the information criterion. Its computer-aided identification algorithm is derived in a very compact form matched to the recurrence processing of succesive observations. Finally, the validity and the effectiveness of our theoretical result are experimentally confirmed through the application to the actual data of environmental noise in room acoustics.
- Publication
- IEICE TRANSACTIONS on transactions Vol.E71-E No.11 pp.1098-1106
- Publication Date
- 1988/11/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- General and Electrical Acoustics
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Mitsuo OHTA, Kazutatsu HATAKEYAMA, Kiminobu NISHIMURA, "A Computer-Aided Identification Method of Arbitrary Stochastic System Contaminated by Modelling Errors and Background Noises" in IEICE TRANSACTIONS on transactions,
vol. E71-E, no. 11, pp. 1098-1106, November 1988, doi: .
Abstract: Generally speaking, in the actual situation of evaluating or predicting the stochastic random phenomena, it is sometimes inevitable to consider the undesirable modelling error generated from an incomplete situation in the actual measurement (i.e., the observations in an actual environmental field are very often given under the unsatisfactory situation without keeping the idealized physical property assumed abstractively in the theoretical research), together with the inevitable additional background noise of arbitrary distribution type. Thus, the unified statistical treatment for the resultant data contaminated by these additional noises of different type is newly proposed in this paper from the system-theoretical viewpoint closely related to environmental field, based on Bayes' theorem and the information criterion. Its computer-aided identification algorithm is derived in a very compact form matched to the recurrence processing of succesive observations. Finally, the validity and the effectiveness of our theoretical result are experimentally confirmed through the application to the actual data of environmental noise in room acoustics.
URL: https://global.ieice.org/en_transactions/transactions/10.1587/e71-e_11_1098/_p
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@ARTICLE{e71-e_11_1098,
author={Mitsuo OHTA, Kazutatsu HATAKEYAMA, Kiminobu NISHIMURA, },
journal={IEICE TRANSACTIONS on transactions},
title={A Computer-Aided Identification Method of Arbitrary Stochastic System Contaminated by Modelling Errors and Background Noises},
year={1988},
volume={E71-E},
number={11},
pages={1098-1106},
abstract={Generally speaking, in the actual situation of evaluating or predicting the stochastic random phenomena, it is sometimes inevitable to consider the undesirable modelling error generated from an incomplete situation in the actual measurement (i.e., the observations in an actual environmental field are very often given under the unsatisfactory situation without keeping the idealized physical property assumed abstractively in the theoretical research), together with the inevitable additional background noise of arbitrary distribution type. Thus, the unified statistical treatment for the resultant data contaminated by these additional noises of different type is newly proposed in this paper from the system-theoretical viewpoint closely related to environmental field, based on Bayes' theorem and the information criterion. Its computer-aided identification algorithm is derived in a very compact form matched to the recurrence processing of succesive observations. Finally, the validity and the effectiveness of our theoretical result are experimentally confirmed through the application to the actual data of environmental noise in room acoustics.},
keywords={},
doi={},
ISSN={},
month={November},}
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TY - JOUR
TI - A Computer-Aided Identification Method of Arbitrary Stochastic System Contaminated by Modelling Errors and Background Noises
T2 - IEICE TRANSACTIONS on transactions
SP - 1098
EP - 1106
AU - Mitsuo OHTA
AU - Kazutatsu HATAKEYAMA
AU - Kiminobu NISHIMURA
PY - 1988
DO -
JO - IEICE TRANSACTIONS on transactions
SN -
VL - E71-E
IS - 11
JA - IEICE TRANSACTIONS on transactions
Y1 - November 1988
AB - Generally speaking, in the actual situation of evaluating or predicting the stochastic random phenomena, it is sometimes inevitable to consider the undesirable modelling error generated from an incomplete situation in the actual measurement (i.e., the observations in an actual environmental field are very often given under the unsatisfactory situation without keeping the idealized physical property assumed abstractively in the theoretical research), together with the inevitable additional background noise of arbitrary distribution type. Thus, the unified statistical treatment for the resultant data contaminated by these additional noises of different type is newly proposed in this paper from the system-theoretical viewpoint closely related to environmental field, based on Bayes' theorem and the information criterion. Its computer-aided identification algorithm is derived in a very compact form matched to the recurrence processing of succesive observations. Finally, the validity and the effectiveness of our theoretical result are experimentally confirmed through the application to the actual data of environmental noise in room acoustics.
ER -
English
