A novel learning algorithm which makes multilayer neural networks multiple-weight-fault tolerant
We propose an efficient algorithm for making multi-layered neural networks (MLN) fault-tolerant to all multiple weight faults in a multi-dimensional interval by injecting intentionally two extreme multi-dimensional values in the interval into the weights of the selected multiple links in a learning phase. The degree of fault-tolerance to a multiple weight fault is measured by the number of essential multiple links. First, we analytically discuss how to choose effectively the multiple links to be injected, and present a learning algorithm for making MLNs fault tolerant to all multiple (i.e., simultaneous) faults in the interval defined by two multi-dimensional extreme points. Then it is proved that after the learning algorithm successfully finishes, MLNs become fault tolerant to all multiple faults in the interval. It is also shown that the time in a weight modification cycle depends little on multiplicity of faults k for small k. These are confirmed by simulation.
learning algorithm multilayer neural network fault tolerance multiple weight fault fault injection