Confidence Limits Analysis (CLA)


Modelling and simulation of real-life systems relies on having a sufficiently good knowledge of the plant and its environment, so that the simulated behaviour can match the real one. Unfortunately, it is frequently the case that the knowledge of system components and the system environment is limited, which makes it impossible to produce simulated results that reflect the behaviour of the physical system.

This research has developed a novel methodological approach to modelling uncertain systems. Rather than trying to optimise scalar estimates of individual system states we have developed interval estimation which explicitly captures the uncertainty inherent in the system. The interval estimates are in effect sets of all feasible states corresponding to a given level of uncertainty in the system. The sets are presented in the form of lower and upper bounds on individual state variables, and hence provide limits on the potential error of each variable. We refer to this state estimation as the Confidence Limits Analysis (CLA). In what follows we present the derivation of the CLA using a water distribution system as a reference.

The system model

Confidence Limit Analysis

Monte Carlo method

Linearised confidence limit analysis

Linear programming method

Sensitivity matrix method

Ellipsoid method

Real-life application



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Last update: 7/05/96