Then, the developed model identification procedure, supported in a robust optimization algorithm, is presented.
The model identification procedure on the basis of cyclic tension/compression tests combined with inverse analysis is described first.
Agustín clearly understood that a reliable and efficient automatic model identification procedure was needed for the large-scale use of the method.
The reliability assessment procedure, which considers a probabilistic model for the structure in analysis, is then introduced, incorporating the results of the model identification procedure.
This paper describes a model identification procedure for identifying an electro-thermal model of lithium ion batteries used in automotive applications.
In order to provide an adequate and thorough background to this technique, a detailed description of the key elements of a model identification procedure (the model itself, the experiment, the statistical tools, etc).
However, the effectiveness of the model identification procedure is strictly related to the execution of properly designed experiments, allowing elucidation of the reaction mechanisms and providing a precise estimation of the kinetic parameters.
Given a model identification procedure able to give accurate output system estimates, e.g. a neural network approximation, we use another feedforward neural network to generate at each time step a constrained optimal control.
The model identification procedure (see Algorithm 3) can be described as follows: for a query chemical compound q and a given chemical space – 1) create the set V of pairs (d i,e i m ), 2) find the Pareto set for V, 3) select the most suitable model for q.
In addition, interested readers are referred to the works by Vera et al. (2010) where the tool was used in the modelling of the MEK/ERK/RKIP pathway or Balsa-Canto et al. (2010) where the complete model identification procedure is applied to a NFκB signaling module model.
