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In this framework, perception corresponds to optimizing the model by changing synaptic activity and connection strengths to minimize prediction errors.
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In addition to optimizing the models, the tool also converts them into a new format to prevent compatibility issues and a local runtime on the devices where the model then runs the execution.
Particle swarm optimization is used to optimize the model parameters.
In order to optimize the model performance, a recently developed optimization technique based on rate rules (Cai and Pitsch, 2014) is extended in this study.
In particular, when the realized asset returns are less than the estimates used to optimize the model, the realized portfolio return will be less than the optimal portfolio return given by the objective.
The user does not need to optimize the model parameters.
The data selection process was set to optimize the model time resolution.
Genetic algorithm and the conjugated gradient method are combined to optimize the model parameters.
This technique uses the characteristics of the signal itself to optimize the model parameters and structure.
After that, the genetic algorithm is used to optimize the model.
Some of these batches were used to optimize the model while the remaining was used to test the methodology.
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