Online Training for Open Faulty RBF Networks

Recently, a batch mode learning algorithm, namely optimal open weight fault regularization (OOWFR), was developed for handling the open fault situation. In terms of the Kullback–Leibler divergence, this batch mode learning algorithm is optimal. However, the main disadvantage of this batch mode learning algorithm is that it requires to store the entire input–output history. Therefore, the memory consumption is a problem when the number of training samples is large. In this paper, we present an online version for the OOWFR algorithm. We consider two learning rate cases, fixed learning rate and adaptive learning rate. We present the convergent conditions for these two cases. Simulation results show that the performance of the proposed online mode learning algorithm is better than that of other online mode learning algorithms. Also, the performance of the proposed algorithm is close to that of the batch mode OOWFR algorithm.