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Diagnosis and reconfiguration of quantised systems
This approach is presented with a strong theoretical background. The given methods are developed for engineering applications and are illustrated with numerous graphic and practical examples. In the first part of the book, new results in the area of automata theory, such as the solution to supervision problems for stochastic automata, are presented as well as an elaborated study on automata networks.
Discrete-event modelling and diagnosis of quantised systems
The second part presents a new approach to qualitative modelling of dynamical systems based on quantized systems. This methodology opens the path towards application and is described and illustrated in detail.
In conclusion practical applications of the developed methods are demonstrated. Select Parent Grandparent Teacher Kid at heart. For example, the proposed method has to be applied to many other actual data of sound environment.
Furthermore, the proposed theory has to be extended to more complicated situations involving multi-signal sources, and an optimal number of expansion terms in the proposed estimation algorithm of expansion type has to be found. The author is grateful for valuable suggestions at Inter-Noise . Wiley, New York. Springer-Verlag, Berlin. Prentice-Hall, New Jersey. Proceedings of the American Control Conference, 6, Acustica, 55, International Journal of Acoustics and Vibration, 21, Journal of Software Engineering and Applications, 10, The Institute of Electrical Engineers of Japan, , Journal of Computational and Graphical Statistics, 5, International Measurement Confederation, 21, Share This Article:.
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Diagnosis and reconfiguration of quantised systems | SpringerLink
Go Back HomePage. DOI: Hisako Orimoto , Akira Ikuta. In order to estimate several evaluation quantities for specific signal based on the observed noisy data, it is fundamental to estimate the fluctuating wave form of the specific signal. On the other hand, the observation data are very often measured in a digital level form at discrete times. This is because some signal processing methods by utilizing a digital computer are indispensable for extracting exactly various kinds of statistical evaluation for the specific signal based on the quantized level data. In this study, a Bayesian filter matched to the complicated sound environment system is derived.
First, in the real situation where the sound environment system is affected by background noise of arbitrary probability distribution, a stochastic system model with quantized observation is established. Next, two types of the recursive algorithm of Bayesian filter to estimate the unknown specific signal are theoretically proposed in the quantized level form. Finally, the effectiveness of the proposed theory is experimentally confirmed by applying it to the estimation problem of real sound environment.
Introduction In the real sound environment, the observation data are usually contaminated by additional external noise i. Establishment of Bayesian Filter with Quantized Observation 3. Estimation Algorithm by Introducing Particles Though the particle filter is useful for the state estimation problem of non-linear systems, this filter needs very complicated algorithm and a large number of computational times based on Monte Carlo simulation and the resampling procedure .
Application to Sound Environment In order to examine the practical usefulness of the proposed Bayesian filter based on the quantized observation, the proposed method is applied to the actual sound environmental data. In this estimation, the finite number of expansion Figure 1. Conflicts of Interest The authors declare no conflicts of interest. Cite this paper Orimoto, H.
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When using nume- calmodelsliketransferfunctionsordi? Not so here, where the system is described on a qualitative level on which uncertainties are naturally incorporated. The book presents a new way to describe systems for supervision. Preparing this book I learned that the key to solve supervision problems is simplicity.
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From the Back Cover The book concerns quantised systems which emerge from continuous-variable systems by quantising the values of all signals. Read more. Tell the Publisher!
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