基于粒子群优化的神经网络训练算法在产品种类预测中的应用

市场环境的变化导致产品更新换代加快，产品种类预测成为新的难题。传统的线性预测方法只能对产品需求的数量或价格等数值进行预测，而无法对产品的发展趋势和未来种类做出正确预测。通过对产品种类预测、数据挖掘和粒子群优化算法的研究，建立种类预测模型，利用基于粒子群优化的神经网络训练算法进行产品种类预测，并以手机为例进行预测，结果证明该方法是有效的。

Category forecast application of neural network algorithm trained by particle swarm optimization

Market changes have shortened product life cycles,therefore category forecast has turning out to be a new difficult problem.But traditional linear forecasting methodology could only make predictions on numeric value such as requested quality and price,and it could not accurately predict product development trend and future categories.Category forecast model was constructed based on the study of product category prediction,data mining and Particle Swarm Optimization(PSO).As the result,product category forecast could be conducted on neural network algorithm trained by PSO.Finally an example of category forecast on mobile telephone was used to verify the effectiveness of the proposed methodology.