Abstract: Artificial neural network is introduced into prediction of cutting tool life.Structural designing of artificial network is always a trouble problem without systematic rule and local minimum usually connects with conventional grads based on parameters optimization.Aiming at the drawback in classical BP artificial networks and combining with Differential Evolution（DE） algorithms，this paper puts forwards the prediction model based on real number coded DE-BP artificial networks.As a result，it provides theoretical basis for the establishment of cutting tool requirements planning，the account of its cost and the selection of machining parameters，as well as reduces the cost in manufacturing executing system.

Key words: cost, neural network, Differential Evolution（DE）, prediction

摘要： 引入BP神经网络算法对产品成本进行预测，建立了产品成本预测模型。针对神经网络参数优化容易陷入局部最优解的缺陷，结合差异演化算法，提出了DE-BP神经网络预测模型。实验表明，该算法具有较高的预测精度，能够为企业生产运营提供可靠的依据。

关键词: 成本, 神经网络, 差异演化, 预测