H-infinity filtering method for neural network training and pruning
An efficient training and pruning method based on the H infinity filtering algorithm is proposed for feedforward neural networks (FNN). A FNN's weight importance measure linking up prediction error sensitivity obtained from the H infinity filtering training, and then a weight salience based pruning algorithm is derived. Moreover, based on the monotonicity property of the H infinity filtering Riccati equation and the initial value of the error covariance matrix, performance of the H infinity filtering training algorithm will also be investigated. The simulation results show that our approach is an effective training and pruning method for neural networks.
h-infinity filtering training pruning feedforward neural network