Weight minimization approach for fault tolerant multi-layer neural networks

We propose a new learning algorithm to enhance fault tolerance of multilayer neural networks (MLN). This method is based on the idea that strong connections make MLN sensitive to faults. To eliminate such connections, we introduce the new evaluation function for the new learning algorithm. It consists of not only the output error but also the sum of all squared weights. With the new evaluation function, the learning algorithm minimizes not only output error but also weights. The value of parameter to balance effects of these two terms is decided actively during training of MLN. Next, to show the effectiveness of the proposed method, we apply it to pattern recognition problems. It is shown that the miss recognition rate and the activity of hidden units are improved