Enhancing Fault Tolerance of Radial Basis Functions

The challenge of future nanoelectronic applications, e.g. in quantum computing or in molecular computing, is to assure reliable computation facing a growing number of malfunctioning and failing computational units. Modeled on biology artificial neural networks are intended to be one preferred architecture for these applications because their architectures allow distributed information processing and, therefore, will result in tolerance to malfunctioning neurons and in robustness to noise. In this work, methods to enhance fault tolerance to permanently failing neurons of Radial Basis Function networks are investigated for function approximation applications. Therefore, a relevance measure is introduced which can be used to enhance the fault tolerance or, on the contrary, to control the network complexity if it is used for pruning.