Towards a Quantum based GA Search for an Optimal Artificial Neural Networks Architecture and Feature Selection to Model NOx Emissions: A Case Study
| Autor: | Mazen Azzam, Mariette Awad, Joseph Zeaiter |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Network complexity
Mathematical optimization Linear programming Artificial neural network Computer science 020209 energy Activation function Feature selection 02 engineering and technology Generalization error Bayesian interpretation of regularization Reduction (complexity) Local optimum Approximation error Genetic algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing |
| Zdroj: | CEC |
| DOI: | 10.1109/cec48606.2020.9185508 |
| Popis: | This paper describes the methodology used to design a NO x predictive emissions monitoring system (PEMS) based on an artificial neural network (ANN). To find the optimal ANN architecture, a QGA-based search was performed over the set of possible model architectures, the activation function in each layer and the learning rate were architecture parameters taken into consideration. In addition, the QGA performed feature selection to remove non-significant input parameters that did not contribute significantly to the output. The objective function included penalties for network complexity and generalization error, among which a newly introduced penalty that makes use of the effective number of parameters provided by Bayesian Regularization. The developed framework was tested on data collected from a power plant consisting of twelve diesel engine-powered generators. It was found that the newly introduced penalty was enough to yield well-performing ANN’s who demonstrated superior performance when compared to some traditional ML models, with up to a 75% reduction in the mean relative error when compared with a normal radial-basis network. In addition, the QGA was better at avoiding local optima, and on average converged in half the time required for the classical GA to converge. |
| Databáze: | OpenAIRE |
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