Identification of semi-parametric hybrid process models

Identification of semi-parametric hybrid process models

Hybrid models are mathematical models that comprise both mechanistic and black-box or datadriven components. Typically, the parameters in the mechanistic part of a hybrid model (if any) are assumed to be known. However in this research, a two-level approach is proposed for the identification of hybrid models where some parameters in the mechanistic part of the model are unknown. At the first le...

Bayesian Identification of Semi-Parametric Binary Response Models

In this paper, minimal conditions under which a semi-parametric binary response model is identified in a Bayesian framework are presented and compared to the conditions usually required in a sampling theory framework. Running headline: Semi-parametric Binary Response Models.

New Approaches to the Identification of Semi-mechanistic Process Models

In process engineering, mostly first-principles models derived from dynamic mass, energy and momentum balances are used. When the process is not perfectly known, the unknown parts of the first principles model should be represented by black-box models, e.g. by neural networks. This paper is devoted to the identification and application of such hybrid models. For the identification of the neural...

Identification in Parametric Models

Comparison of parametric and semi-parametric binary response models

A Bayesian semi-parametric estimation of the binary response model using Markov Chain Monte Carlo algorithms is proposed. The performances of the parametric and semi-parametric models are presented. The mean squared errors, receiver operating characteristic curve, and the marginal effect are used as the model selection criteria. Simulated data and Monte Carlo experiments show that unless the bi...