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Practical issues in distributed parameter estimation: Gradient computation and optimal experiment design
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DOI：10.1016/0967-0661(96)00169-4.png)
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摘要
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Parameter estimation in nonlinear distributed-parameter systems is usually accomplished by minimizing an output least-square criterion, which is defined implicitly through the solution of the model equations. This paper addresses itself to two important practical issues of the parameter-estimation procedure, i.e., the numerical procedure used to compute the gradient of the criterion with respect to the unknown parameters, and the selection of experimental conditions, i.e., sensor locations and input signals. An experiment design procedure based on the sensitivity matrix is presented. The methods for gradient computation and experiment design have been successfully applied to several process models, and are illustrated in this paper with a simple heat-conduction problem and a more complex model of a catalytic fixed-bed reactor.
Keyword:
identification
sensitivity analysis
parameter estimation
distributed-parameter systems
numerical methods
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