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But with the future of the euro zone uncertain, engineering can't make up for investor skepticism.
Hence these uncertainties must be considered in handling the optimization problems of uncertain engineering structures [3, 4].
The paper presents a fractional moment method for probabilistic lifetime modelling of uncertain engineering systems.
Neural networks have provided an ideal framework for online identification and control of many complex uncertain engineering systems because of their great flexibility in approximating a large class of continuous maps and their adaptability due to their inherently parallel architecture.
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The capabilities of the approaches selected are addressed in view of realistic modeling and processing of uncertain quantities in engineering.
The stochastic gamma process has been widely used to model uncertain degradation in engineering systems and structures.
The uncertain parameters in engineering design may appear as random or intervals based on the information available from the past history or physical experiments.
Although the system parameters or properties are uncertain in many engineering problems, but their probable range of values i.e. upper and lower bounds, can be provided from practical experience and engineering knowledge.
On the basis of the equivalent variables, an uncertainty quantification model is established, in which the unified probabilistic information related to the uncertain responses of engineering systems can be computed with the aid of the fuzziness discretization and reconstruction, the belief and plausibility measures analysis, and the interval response analysis.
There exist a great amount of uncertain factors in actual engineering.
However, it is often difficult or computationally expensive to determine the probabilistic distributions of uncertain factors in many engineering problems [17].
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