基于IIGA-BP神经网络的钢材销售预测模型

为克服传统BP神经网络(BP Neural Network,BPNN)在销售预测中,预测精度低、收敛速度慢的缺点.提出了一种基于改进免疫遗传算法(Improved Immune Genetic Algorithm,IIGA)优化BP神经网络的销售预测模型.改进的免疫遗传算法提出了新的种群初始化方式、抗体浓度的调节机制及自适应交叉算子、变异算子的设计方法,有效的提高了IIGA的收敛能力和寻优能力.并用IIGA优化BPNN的初始权值和阈值,改善网络参数的随机性导致BPNN输出不稳定和易陷入局部极值的缺点.以某钢铁企业的历史销售数据为例进行实证研究,利用Matlab分别构建BP、IGA-BP和IIGA-BP神经网络预测模型进行仿真对比分析.实验证明,IIGA-BP神经网络预测模型较BP神经网络预测模型预测精度提高了23.82％,较IGA-BP神经网络预测模型预测精度提高了22.02％.IIGA-BP神经网络模型对钢材销售预测的泛化性能更好,预测效果更稳定误差基本保持在0.25,0.25]之间,预测精度大幅度提高,为企业销售预测提供了一种较为有效的方法.

Sales Forecasting Model for Steel Product Based on IIGA-BP Neural Network

To eliminate the shortcomings of low precision and slow convergence in the sales forecasting based on the traditional BP Neural Network (BPNN), this study proposes a new model based on an Improved Immune Genetic Algorithm (IIGA) optimized BP neural network. IIGA presents a new way of population initialization, a regulatory mechanism of antibody concentration, and a design method of adaptive crossover operators and mutation operators. Therefore, the convergence ability and global search ability of IIGA are greatly improved. In addition, IIGA can optimize the initial weights and thresholds of the BP neural network and overcome the drawbacks of output instability of the BP neural network and proneness to local minimum induced by the randomness of network parameters. With the past records of sales figures in a steel enterprise as an example, the BP, IGA-BP and IIGA-BP neural network forecasting models are built with Matlab for simulation comparison. The experiments demonstrate that the precision of the IIGA-BP model is 23.82% higher than that of the BP model and 22.02% higher than that of the IGA-BP model. The IIGA-BP model possesses better generalization about steel sales forecasting and more stable forecasting, with errors basically in the range of -0.25 to 0.25, and its forcasting precision is dramatically improved. The proposed model provides a more effective method for sales forecasting in enterprises.