Germinal Center Optimization Applied to Recurrent High Order Neural Network Observer. Issue 13 (2018)

• Record Type: Journal Article
• Authors: Rios, Jorge D.; Villaseñor, Carlos; Alanis, Alma Y.; Arana-Daniel, Nancy; Lopez-Franco, Carlos
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 13(2018)
• Page Start: 332
• Page End: 337
• Publication Date: 2018
• Subjects: Adaptive algorithms -- Parameter optimization -- Neural networks -- State estimation -- Extended Kalman filters -- Modelling -- Time-delay; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.07.300
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In this work, a germinal center optimization (GCO) algorithm which implements temporal leadership through modeling a non-uniform competitive-based distribution for particle selection is used to find an optimal set of parameters for a recurrent high order neural network observer (RHONNO). The RHONNO is trained with an extended Kalman filter algorithm and it is capable of giving a model of the system besides of just giving state estimation. Furthermore, the RHONNO does not need previous knowledge of the system model, nor measurements, estimation or bounds of delays and disturbances. Applicability of the proposed methodology is presented using simulation results.