A New Convex Hull, Sliding Window Based Online Adaptation Method. Issue 10 (2018)

• Record Type: Journal Article
• Authors: Khosravani, H.; Ruano, A.; Ferreira, P.M.
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 10(2018)
• Page Start: 211
• Page End: 216
• Publication Date: 2018
• Subjects: Convex Hull -- Multi Objective Genetic Algorithm -- Online Adaptation Process -- Radial Basis Function Neural Networks -- Time Series Models; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.06.264
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In any online adaptation scheme, two important phenomena should be taken into consideration; parameter shadowing and parameter interference. To alleviate these problems, in this paper a convex hull, sliding window based online adaptation method for fixed-structure Neural Networks is proposed. The method is capable of dealing with the two phenomena, presenting better results than known alternatives. An analysis of the real-time run time and memory consumption of the algorithm demonstrates that it can be used for real-time applications.