A New Convex Hull, Sliding Window Based Online Adaptation Method. Issue 10 (2018) - Khosravani, H.; Ruano, A.; Ferreira, P.M.
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.