Quaternion recurrent neural network with real-time recurrent learning and maximum correntropy criterion

arXiv:2402.14227v1 Announce Type: new
Abstract: We develop a robust quaternion recurrent neural network (QRNN) for real-time processing of 3D and 4D data with outliers. This is achieved by combining the real-time recurrent learning (RTRL) algorithm and the maximum correntropy criterion (MCC) as a loss function. While both the mean square error and maximum correntropy criterion are viable cost functions, it is shown that the non-quadratic maximum correntropy loss function is less sensitive to outliers, making it suitable for applications with …

abstract algorithm arxiv criterion cs.lg data function loss network neural network outliers processing real-time real-time processing recurrent neural network robust type