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Design of neural networks to tolerate the mixture of two types of faults

The authors present a design method of neural networks for optimization problems, which can tolerate the simultaneous existence of both stuck-at-zero and stuck-at-one faults. By using this new design method together with one presented earlier, neural networks can tolerate very well the mixture of the both types of faults as well as unidirectional faults.