Combining Stochastic Competitive Scheme and Hysteresis Quantized Neuron for Reliability Optimization

System reliability is an important design measure in many systems engineering fields. In this paper, we propose a new neural network method combining stochastic competitive scheme and hysteresis quantized neuron for the reliability optimization. In the proposed algorithm, the neurons are divided into two classes: One is binary neurons with stochastic competitive scheme and the other is quantized neuron with hysteresis. The competitive scheme always provides a feasible solution and search space is greatly reduced without a burden on the parameter tuning. Furthermore, the stochastic dynamics and hysteresis can help the neural network escape from local minima, and therefore the proposed algorithm can get better results than other neural network method.