Metacognitive Sedenion-Valued Neural Network and its Learning Algorithm

Abstract

In this article, a metacognitive sedenion-valued neural network (Mc-SVNN) and its learning algorithm are proposed. Its application to diverse time-series prediction problems is presented. The Mc-SVNN contains two components: a sedenion-valued neural network that represents the cognitive component, and a metacognitive component, which serves to self-regulate the learning algorithm. At each epoch, the metacognitive component decides what, how, and when learning occurs. The algorithm deletes unnecessary samples and stores only those that are used. This decision is determined by the sedenion magnitude and the 15 sedenion phases. The Mc-SVNN is applied to four real-world forecasting problems: USD-to-euro currency exchange rate forecasting, the sunspot number time series, power demand forecasting, and daily temperature prediction in Abu Dhabi. Compared to existing methods, the Mc-SVNN demonstrates superior performance in time-series forecasting while using a smaller number of parameters.