Part of Advances in Neural Information Processing Systems 8 (NIPS 1995)
Visakan Kadirkamanathan, Maha Kadirkamanathan
In this paper, recursive estimation algorithms for dynamic modular networks are developed. The models are based on Gaussian RBF networks and the gating network is considered in two stages: At first, it is simply a time-varying scalar and in the second, it is based on the state, as in the mixture of local experts scheme. The resulting algorithm uses Kalman filter estimation for the model estimation and the gating probability estimation. Both, 'hard' and 'soft' competition based estimation schemes are developed where in the former, the most probable network is adapted and in the latter all networks are adapted by appropriate weighting of the data.