Analysis of Fault Tolerance of Cellular Neural Networks and Applications to Image Processing

This paper initiates the study of fault tolerant properties of faulty cellular neural networks (CNNs). A class of CNNs with single faulty cell is established, which is successfully applied to image processing: noise removing and image reversion. A series of computer simulations demonstrate CNNs have satisfied fault tolerant properties for image processing.