Fault tolerance of artificial neural networks with applications in critical systems
Peter Protzel, Daniel Leix Palumbo, Michael K. Arras · 1992
One of the key benefits of future hardware implementations of certain artificial neural networks (ANN's) is their apparently "built-in" fault tolerance which makes them potential candidates for critical tasks with high reliability requirements. This paper investigates the fault-tolerance characteristics of time-continuous, recurrent ANN's that can be used to solve optimization problems. The principle of operation and the performance of these networks are first il lustrated by using wel l- known model problems like the traveling salesman prob lem and the assignment problem. The ANN's are then subjected to up to 13 simultaneous "stuckat -1" or "stuck-at-0" faults for network sizes of up to 900 "neurons." The effect of these fau lts on the performance is demonstrated and the cause for the observed fau lt tolerance is discussed. An application is presented in which a network performs a critical task for a realtime distributed processing system by generating new task allocations during the ...