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A general automated model parameter optimizer has been developed within AZOrange.
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A process optimization system, which consists of a steady-state process model, a model parameter estimator and a process optimizer for a fluidized catalytic cracking (FCC) process under full and partial combustion modes, is presented in this paper.
Then, the resulting steady-state model was utilized to develop the model parameter estimator and the process optimizer both of which adopt a successive quadratic programming algorithm to efficiently locate the optimum solutions.
Using the "Parameter Optimizer" widget to optimize the parameters of any AZOrange learner.
The "Parameter Optimizer" widget returns an Orange learner object with optimized rather than default model parameters.
The actual model is built by connecting the "Train Learner" widget with the "Parameter Optimizer" and the train data set.
It consists of an online steady-state optimizer, a model predictive controller (MPC), and a model parameter estimator.
Even though the parameter optimizer handles any number of parameters simultaneously, a comprehensive optimization would in general be far too computationally expensive.
In the "Parameter Optimizer" widget interface the sampling technique used to assess the generalization accuracy at each model parameter configuration can be selected.
The parameters displayed on the right hand side depend on the learner collected to the "Parameter Optimizer" widget.
The C and epsilon parameters were determined using the built-in parameter optimizer of the SVMTorch library that iterates over several possible values to pick the optimal choice.
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