Fault-tolerance of basis function networks using tensor product stabilizers

Neural networks are intended to be used in future nanoelectronics since these architectures seem to be fault-tolerant to malfunctioning elements and robust to noise. In this paper, the robustness to noise of basis function networks using tensor product stabilizers is analyzed and upper bounds of the mean square error under noise contaminated weights or inputs are determined. Furthermore, consequences of permanently malfunctioning neurons are investigated and their impact on the mean squared error is analyzed. To achieve a reliable operation of the neural network necessary restrictions are introduced. Finally, the impact of technical realizations is investigated and its complexity is compared to radial basis functions.