Dissertation > Excellent graduate degree dissertation topics show

Research on Promotional Sales Forecast Based on PCA and Particle Swarm Optimized BP Neural Network

Author: BiJianTao
Tutor: WeiHongQin
School: Donghua University
Course: Management Science and Engineering
Keywords: Sales Promotion Demand Forecast BP Neural Network Principal Components Analysis (PCA) Particle Swarm Optimization (PSO)
CLC: F274
Type: Master's thesis
Year: 2012
Downloads: 135
Quote: 0
Read: Download Dissertation

Abstract


Sales promotion plays an important role in modern enterprises marketing strategies. With the growing trend of consumption diversity and products homogenizationretailers are investing more and more in sales promotion of terminal market in order to inspire peolpe’s desire to purchase.Retailers’ promotion activities increase the uncertainty of market demand to a certain degree and make it more difficult to forecast demand of promotional products both promptly and accurately.In the light of the relative short life cycle and seasonal feature of apparel products theory of artificial neural network (ANN) was introduced to the domain of products demand forecast and a hybrid forecasting model was established to predict monthly sales of a specific men’s shirt.Firstly, this paper made a systematic study on the theory of promotional demand factors and eight different factors was selected including seasonality product life cycle, promotion price discount, festival days, advertising investment and so on and those different factors were also quantified in order to measure the influential degree on products sales in a quantitative manner.Then, principal components analysis was made using SPSS statistical analysis software to decrease dimensions of input datas and eliminate the multicollinearity of different factors.Due to the limitations of its own learning mechanisms standard back propagation neural network (BPNN) may get into a local optimal value and take a relatively longer time to train the net.In order to overcome the limitations of the algorithm a hybrid forecasting model was established which combined principal components analysis(PCA), BP neural network and particle swarm optimization algorithm (PSO), aiming at improving BP neural network both from the aspects of sample data quality and initial parameters of BP neural network——Firstly, principal components analysis was used to reduce the dimensions of promotional factors and took the extracted components as the new input of the net in order to simplify the structure of network and improve the generalization ability. Secondlyparticle swarm optimization algorithm was introduced to train the initial parameters of the BP network including initial weights and bias values, aiming to decrease networks training time and improve the forecasting accuracy.Then simulation analysis was made on the basis of the fully trained forecasting model.Finally, an example analysis was made based on monthly sales datas of a specific men’s shirt of brand S and the performances of different forecast models were also compared. in order to verify the validation of this model The results showed that the proposed model simplified the architecture of BP network decreased the training time of the network and to a certain degree improved the forecast accuracy.This proposed model can be used as a feasible solution to multi-factors forecasting problems and has a significant theoretical and practical value.

Related Dissertations

  1. Research on Feature Extraction and Classification of Tongue Shape and Tooth-Marked Tongue in TCM Tongue Diagnosis,TP391.41
  2. Research on Visual Servo System of Mechanical ARM,TP242.6
  3. Municipal tourism land use planning environmental impact assessment,X820.3
  4. Study on Taste Characteristic of Taste Peptide Enzymatic Production from Oyster Base on A Neural Network Method,TS254.4
  5. The Research on Evaluation of Living Status Systems of Expressway Relocated People,D523
  6. Mine Risk Information Integration and Intelligent Early Warning,X936
  7. Research of Virus Detection Methods Based on Multiple Anti-virus Softwares Collaboration,TP309.5
  8. Research of Orange Quality Classification Technology Based on Computer Vision,TP391.41
  9. A Comparative Study on Politeness Strategies in Chinese and American Sales Promotion Ads of Consumer Goods,H313
  10. Optimization Study on Gating System and Molding Process Parameters of Injection Mold Based on Simulation,TQ320.662
  11. Study on Fabric Defect Detection and Sutomati Grad-ing System,TP391.41
  12. Research and Design of Global Vision in Small-Sized Intelligent Soccer Robot System,TP242.6
  13. Research of Adaptive Active Noise Control Based on Neural Network,TP183
  14. Modeling and Simulation Software Design for the Glutamic Acid Fermentation,TQ922.1
  15. The Applicability of Data Mining Technology for Environmental Protection Management System,TP311.13
  16. Research on Automatic Reading System for Digital Meters,TP391.41
  17. Research on Feature Extraction, Selection and Classification Algorithms for Pulmonary CAD,TP391.41
  18. Research on State Diagnosis on Fan Based on Factor Analysis and BP Neural Network,F426.61
  19. Based on Computer Vision Fabric defect detection and classification of,TP274
  20. Research on Reservoir-induced Seismic Risk Assessment Based on Neural Network and Genetic Algorithm,TV697.24
  21. Multiple-target Optimization Design of Pre-forging for Gear Blank,TG312

CLC: > Economic > Economic planning and management > Enterprise economy > Enterprise Marketing Management
© 2012 www.DissertationTopic.Net  Mobile