A Modified Hopfield Neural Network for Diagnosing Comparison-Based Multiprocessor Systems Using Partial Syndromes

A modified Hopfield neural network is introduced to solve the comparison-based system-level fault diagnosis problem when only partial syndromes are available. We use the generalized comparison model, where a set of tasks is assigned to pairs of nodes and their outcomes are compared by neighboring nodes. To identify the set of permanently faulty nodes, the collections of all agreements and disagreements, i.e., the comparison outcomes, are used. First, we show that the new diagnosis approach works correctly when t-diagnosable systems are considered. Then, we show the main contribution of this new diagnosis approach which is its capability of correctly identifying the set of faulty nodes when not all the comparison outcomes are available to the diagnosis algorithm at the beginning of the diagnosis phase, i.e., partial syndromes. The simulation results indicate that the modified Hopfield neural network-based fault identification algorithm provides an effective solution to the system-level fault diagnosis problem even when partial syndromes are available.