Application of Genetic Neural Network on Lifeless-Repairable Spares Consumption Forecasting

Pointing at the problem that the spares consumption quota has been using the experience to develop, which makes spares application random and blind, this paper puts forward to build the reasonable lifeless-repairable spares consumption quota model. Analyze and determine the factors influencing the lifeless-repairable spares consumption, use BP neural network to predict, and use genetic algorithm to optimize the weights and thresholds of BP neural network, so that the network can obtain the global minimum point. The example shows that the model´s predicted results are relatively accurate and has high practicability.