Regularizers for fault tolerant multilayer feedforward networks

Fault tolerance is an important issue for multilayer feedforward networks (MFNs). However, in the classical training approach for open node fault and open weight fault, we should consider many potential faulty networks. Clearly, if the number of faulty networks considered in the objective function is large, this training approach would be very time consuming. This paper derives two objective functions for attaining fault tolerant MFNs. One objective function is designed for handling open node fault while another one is designed for handling open weight fault. With the linearization technique, each of these two objective functions can be decomposed into two terms, the training error and a simple regularization term. In our approach, the objective functions are computationally simple. Hence the conventional backpropagation algorithm can be simply applied to handle these fault tolerant objective functions.