Neural network cost prediction model based on real-coded genetic algorithm and its application
基于实数编码遗传算法的神经网络成本预测模型及其应用

In production process,many complex factors which influence cost affect each other and the coupling phenomenon exists,so it is important and difficult to predict the cost.By combining genetic algorithm with error back propagation neural network,a hybrid algorithm that trained neural network weight by real-coded adaptive mutation genetic algorithm is presented,and it overcomes the disadvantage that traditional neural network is easy to fall into local minima.The product cost composition is expressed by matrix,the product cost composition model is established,on the basis of the model,the product cost prediction model based on neural network is established,and the interactions among cost factors are taken into account.Furthermore,the model is successfully applied to cost prediction in some iron and steel enterprise,and the prediction precision is improved.