Genetic Algorithms and Sensitivity Analysis Applied to Select Inputs of a Multi-Layer Perceptron for the Prediction of Air Pollutant Time-Series
作者： Harri Niska , Mikko Heikkinen , Mikko Kolehmainen
DOI: 10.1007/11875581_27
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摘要: The aim of this paper was to evaluate genetic algorithms (GA) and sensitivity analysis (SA) for selecting inputs a multi-layer perceptron model (MLP) applied forecast time-series urban air pollutant. main objective compare usability efficiency the methods. results in general showed that methods based on SA GA can be used efficiently select relevant variables thus, enhance performance MLP.
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