Neural network realization of Markov reliability and fault-tolerance models
Abstract A feed-forward recursive neural network consisting of 2n neurons forming 2 layers, n neurons in each layer, is set to represent a discrete-time n-state Markov model of a fault-tolerant hardware. A quadratic energy function for the neural net is presented and the appropriate update equations for the weights are derived using the least mean square gradient-descent technique.