Using a Gaussian mixture neural network for incremental learning and robotics

Abstract

In this work we use IGMN (standing for incremental Gaussian mixture network), an incremental neural network model based on Gaussian mixtures, for on-line control and robotics. IGMN is inspired on recent theories about the brain, specially the memory-prediction framework and the constructivist artificial intelligence, which endows it with some unique features that are not present in most artificial neural network models. Moreover, IGMN learns incrementally from data flows (each data can be immediately used and discarded) and asymptotically converges to the optimal regression surface as more training data arrive. Through several experiments using the proposed model in robotics it is demonstrated that IGMN is not sensitive to initialization conditions, does not require fine-tuning its configuration parameters and has a good computational performance, thus allowing its use in real time control applications.