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• W2000686545 abstract "We explore extensions of optimal experiment design algorithms for large-scale deterministic catalytic kinetic systems, and of system identification and control techniques for stochastic thin film deposition systems. In the case of optimal experiment design, we suggest the use of principal component analysis and clustering to identify similarities in parameters and their sensitivities. This enables the grouping of similar parameters together, thus reducing the dimensionality before applying a D-optimal experimental design, and ensuring identifiability of the selected parameters and better conditioning of the Fischer information matrix. In the case of thin film deposition, since the stochastic deposition process is simulated using kinetic Monte Carlo models, closed form models that can be used for control are not available. We present modifications to proper orthogonal decomposition methods for non-ergodic systems, and methods for reparameterisation of autoregressive integrated moving average models for identification of compact closed form models and model predictive control." @default.
• W2000686545 created "2016-06-24" @default.
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• W2000686545 date "2011-01-01" @default.
• W2000686545 modified "2023-09-22" @default.
• W2000686545 title "Extensions to experiment design and identification algorithms for large-scale and stochastic processes" @default.
• W2000686545 doi "https://doi.org/10.1504/ijamechs.2011.040158" @default.
• W2000686545 hasPublicationYear "2011" @default.
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