Optimization of Neural Network Parameters Based on a Genetic Algorithm for Prediction of Time Series
В.С. Тормозов, Alexander L. Zolkin, K.A. Vasilenko
20202020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon)20 citationsDOI
Abstract
The article proposes a method that allows to solve the complex combinatorial problem of structural optimization of an artificial neural network with a large dimension of the space of optimization parameters using the stochastic mutation function with a constant coefficient of variation. A study on assessment of applicability of this approach to solve the problem is given. This method is proposed to be used for the forecasting of time series values.
Topics & Concepts
Artificial neural networkSeries (stratigraphy)Dimension (graph theory)Computer scienceStochastic neural networkGenetic algorithmTime seriesConstant (computer programming)Optimization problemMathematical optimizationAlgorithmVariation (astronomy)Artificial intelligenceMathematicsRecurrent neural networkMachine learningPaleontologyBiologyAstrophysicsPure mathematicsProgramming languagePhysicsAdvanced Data Processing TechniquesStatistical and Computational ModelingNeural Networks and Applications