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Springer Dependability Modelling under Uncertainty - Imprecise Probabilistic Approach

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Description

This comprehensive academic text introduces uncertainty-preserving dependability methods designed for early design stages in mechatronic systems. The work demonstrates how Dempster-Shafer theory serves as a viable alternative to traditional probability theory when making dependability predictions during initial design phases. As modern mechatronic design processes become increasingly shorter and parallelized due to market pressures, quantitative analysis capabilities in early development are essential for influencing design decisions. The book addresses the challenge of high uncertainty levels when limited data is available, providing researchers, engineers, and students with practical approaches to handling imprecision in dependability assessment. Part of the Studies in Computational Intelligence series (Volume 148), this publication bridges the gap between theoretical uncertainty modelling and real-world engineering applications where incomplete information is the norm.

Key Features

Presents uncertainty-preserving dependability methods applicable to early mechatronic design stages when data availability is limited.

Explores Dempster-Shafer theory as a probabilistic alternative for dependability predictions with imprecise information.

Supports quantitative dependability analysis in parallel design processes under time-to-market constraints.

Part of the Studies in Computational Intelligence series, Volume 148, offering peer-reviewed academic rigour.

Addresses practical challenges in dependability modelling when uncertainty is inherently high due to incomplete design data.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
18 November 2010
Listed Since
14 June 2010

Barcode

No barcode data available

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