ARTIST: ART-2A Driven Generation of Fuzzy Rules for Online Handwritten Gesture Recognition

摘要

Incremental learning, especially when learning from a scratch, has a lot of interest for online gesture recognition. However the lack of learning examplers combined to low computational cost suggests building robust and efficient learning machines. In this paper we introduce a hybrid model of ART-2A neural network combined to Takagi-Sugeno (TS) neuro-fuzzy network. The latter model is applied for online handwritten gesture recognition, when the learning is starting from scratch and no class information, such as gesture type or number of classes, is predefined. Moreover, using ART-2A neural network and our novel distance measure, the computational complexity of the whole model decreases while preserving high accuracy. Furthermore, we exploit the forgetting dilemma of online learning by introducing a competitive Recursive Least Squares method for TS models. Together, all the modeling has shown promising results.