Title :
Spacecraft solar arrays degradation forecasting with evolutionary designed ANN-based predictors
Author :
Semenkina, Maria ; Akhmedova, Shakhnaz ; Semenkin, Eugene ; Ryzhikov, Ivan
Author_Institution :
Institute of Computer Sciences and Telecommunication, Siberian State Aerospace University, Krasnoyarskiy Rabochiy ave., 31, Krasnoyarsk, 660014, Russia
Abstract :
The problem of forecasting the degradation of spacecraft solar arrays is considered. The application of ANN-based predictors is proposed and their automated design with self-adaptive evolutionary and bio-inspired algorithms is suggested. The adaptation of evolutionary algorithms is implemented on the base of the algorithms´ self-configuration. The island model for the bio-inspired algorithms cooperation is used. The performance of four developed algorithms for automated design of ANN-based predictors is estimated on real-world data and the most perspective approach is determined.
Keywords :
Algorithm design and analysis; Artificial neural networks; Evolutionary computation; Neurons; Optimization; Sociology; Statistics; ANN-based Predictors; Automated Design; Bio-Inspired Algorithms Co-operation; Degradation Forecasting; Self-configuring Evolutionary Algorithms; Spacecraft Solar Array;
Conference_Titel :
Informatics in Control, Automation and Robotics (ICINCO), 2014 11th International Conference on