Temporal BYY learning and its applications to extended Kalman filtering, hidden Markov model, and sensor-motor integration

This paper systematically re-elaborates the author´s temporal Bayesian Ying-Yang (TBYY) learning system and theory (1998). First, the previous approximate implementation of TBYY theory by recursive TBYY has been justified from the first order Taylor expansion. Second, an alternative suggestion is given for extending Kalman filtering to nonGaussian noise and nonlinear state space model. Third, other two variants of hidden Markov model (HMM) are proposed for facilitating adaptive learning, with criteria for selecting the number of hidden states. Finally, the recursive TBYY has been applied to the problem of sensor-motor integration, which can be regarded as a probabilistic extension of Kawato´s feedback-error-learning (1990)