Analytical weight shifting models for self-recovery networks

Summary form only given, as follows. An efficient self-recovery technique for neural networks called weight shifting and its analytical models have been proposed. The technique was applied to recover a network when some faulty links and neurons occurred during the operation. The proposed model is suitable for VLSI inclusion with the network in terms of quick recovery time and silicon area.<<ETX>>