山东理工大学学报：自然科学版
山東理工大學學報：自然科學版
산동리공대학학보：자연과학판
Journal of Shandong University of Technology:Science and Technology
2011年
6期
29-33
,共5页
销量预测%主成分分析法%BP神经网络%粒子群优化算法
銷量預測%主成分分析法%BP神經網絡%粒子群優化算法
소량예측%주성분분석법%BP신경망락%입자군우화산법
sales forecasting%principal components analysis（PCA）%back propagation neural network%particle swarm optimization algorithm（PSO）
针对影响产品销量的因素众多,并且影响因素之间相互作用等特点,将人工神经网络理论引入产品销量预测领域.同时,为了克服BP网络的局限性,提出将主成分分析方法、BP神经网络以及粒子群优化算法相结合,分别从样本质量和初始权值两个方面对BP神经网络进行改进.最后,对某品牌服装产品的月销售量进行了实例研究.结果表明,所提出的模型简化了BP网络结构的同时,提高了网络的预测精度,从而验证了模型的有效性.
針對影響產品銷量的因素衆多,併且影響因素之間相互作用等特點,將人工神經網絡理論引入產品銷量預測領域.同時,為瞭剋服BP網絡的跼限性,提齣將主成分分析方法、BP神經網絡以及粒子群優化算法相結閤,分彆從樣本質量和初始權值兩箇方麵對BP神經網絡進行改進.最後,對某品牌服裝產品的月銷售量進行瞭實例研究.結果錶明,所提齣的模型簡化瞭BP網絡結構的同時,提高瞭網絡的預測精度,從而驗證瞭模型的有效性.
침대영향산품소량적인소음다,병차영향인소지간상호작용등특점,장인공신경망락이론인입산품소량예측영역.동시,위료극복BP망락적국한성,제출장주성분분석방법、BP신경망락이급입자군우화산법상결합,분별종양본질량화초시권치량개방면대BP신경망락진행개진.최후,대모품패복장산품적월소수량진행료실례연구.결과표명,소제출적모형간화료BP망락결구적동시,제고료망락적예측정도,종이험증료모형적유효성.
In the light of different factors affecting sales of products and the interaction between those factors,the theory of artificial neural network was introduced into the domain of sales forecasting.At the same time,BP neural netowrk was improved both from the aspects of sample data quality and initial parameters to overcome its limitation by combining principal components analysis（PCA）,BP neural network and particle swarm optimization algorithm（PSO）.Finally,an example analysis was made in order to verify the validation of this model.The results showed that the suggested model simplified the architecture of BP network and improved forecast accuracy.Thereby,the effectiveness of this model was validated.
