Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Integration of fuzzy neural network and artificial immune system-based back-propagation neural network for sales forecasting using qualitative and quantitative data

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Sales forecasting plays a very important role in business operation. Many researches generally employ statistical methods, such as regression or auto-regressive integrated moving average model, to forecast the product sales. However, they only can consider the quantitative data. Some exogenous qualitative variables have more influence on forecasting result. Thus, this study attempts to propose a integrated forecasting system which is able to consider both quantitative and qualitative factors to achieve a more comprehensive result. Basically, fuzzy neural network is first employed to capture the expert knowledge regarding some qualitative factors. Then, it is combined with the time series data using an artificial immune system based back-propagation neural network. A laptop sales data set provided by a distributor in Taiwan is applied to verify the proposed approach. The computational result indicates that the proposed approach is superior to other forecasting methods. It can be used to decrease the inventory costs and enhance the customer satisfaction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Azadeh, A., Moghaddam, M., Khakzed, M., & Ebrahimipour, V. (2012). A flexible neural network-fuzzy mathematical programming algorithm for improvement of oil price estimation and forecasting. Computers & Industrial Engineering, 62, 421–430.

    Article  Google Scholar 

  • Aydin, I., Karakose, M., & Akin, E. (2010). An adaptive artificial immune system for fault classification. Journal of Intelligent Manufacturing, 23(5), 1489–1499.

    Article  Google Scholar 

  • Chang, P. C., Wang, Y. W., & Tsai, C. Y. (2005). Evolving neural network for printed circuit board sales. Expert Systems with Applications, 29(1), 83–92.

    Article  Google Scholar 

  • Dasgupta, D., & Gonzalez, F. (2002). An immunity-based technique to characterize intrusions in computer networks. IEEE Transaction On Evolutionary Computation, 6(3), 281–291.

    Article  Google Scholar 

  • De Castro, L. N., & Timmis, J. (2002a). An artificial immune network for multimodal function optimization. Proceedings of the IEEE World Congress on Evolutionary computation (pp. 699–704).

  • De Castro, L. N., & Timmis, J. (2002b). Artificial immune systems: a novel paradigm to pattern recognition. In Artificial Neural Networks in Pattern Recognition (pp. 67–84), UK: University of Paisley.

  • De Castro, L. N., & Timmis, J. (2003). Artificial immune systems as a novel soft computing paradigm. Soft Computing, 7(8), 526–544.

  • De Castro, L. N., & Zuben, F. J. V. (2002). Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation, 6, 239–251.

    Article  Google Scholar 

  • Deng, W., Chen, R., Gao, J., Song, Y., & Xu, J. (2012). A novel parallel hybrid intelligence optimization algorithm for a function approximation problem. Computers and Mathematics with Applications, 63, 325–336.

    Article  Google Scholar 

  • Diao, Y., & Passino, K. M. (2002). Immunity-based hybrid learning methods for approximator structure and parameter adjustment. Engineering Applications of Artificial Intelligence, 15, 587–600.

    Article  Google Scholar 

  • El-Abd, M. (2012). Performance assessment of foraging algorithms vs. evolutionary algorithms. Information Sciences, 182, 243–263.

    Article  Google Scholar 

  • Fu, X., Li, A., Wang, L., & Ji, C. (2011). Short-term scheduling of cascade reservoirs using an immune algorithm-based particle swarm optimization. Computers and Mathematics with Applications, 62(6), 2463–2471.

    Article  Google Scholar 

  • Hadavandi, E., Shavandi, H., & Ghanbari, A. (2010). Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting. Knowledge-Based Systems, 23, 800–808.

    Article  Google Scholar 

  • Hadavandi, E., Shavandi, H., Ghanbari, A., & Naghneh, S. A. (2012). Developing a hybrid artificial intelligence model for outpatient visits forecasting in hospitals. Applied Soft Computing, 12, 700–711.

    Article  Google Scholar 

  • Hong, W. C. (2010). Application of chaotic ant swarm optimization in electric load forecasting. Energy Policy, 38, 5830–5839.

    Article  Google Scholar 

  • Hornik, K., Stinchocombe, M., & White, H. (1989). Multilayer feed-forward networks are universal approximators. Neural Networks, 2, 359–366.

    Article  Google Scholar 

  • Huang, P. T. B. (2014). An intelligent neural-fuzzy model for an in-process surface roughness monitoring system in end milling operations. Journal of Intelligent Manufacturing. doi:10.1007/s10845-014-0907-6.

  • Jang, J. S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on system, Man, and Cybernetics, 23(3), 665–685.

    Article  Google Scholar 

  • Jansen, T., & Zarges, C. (2011). Analyzing different variants of immune inspired somatic contiguous hypermutations. Theoretical Computer Science, 412, 517–533.

    Article  Google Scholar 

  • Jardin, P. D., & Severin, E. (2012). Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time. European Journal of Operational Research, 221, 378–396.

    Article  Google Scholar 

  • Katherasan, D., Elias, J. V., Sathiya, P., & Haq, A. N. (2014). Simulation and parameter optimization of flux cored arc welding using artificial neural network and particle swarm optimization algorithm. Journal of Intelligent Manufacturing, 25, 67–76.

    Article  Google Scholar 

  • Kovac, P., Rodic, D., Pucovsky, V., & Savkovic, B. (2013). Application of fuzzy logic and regression analysis for modeling surface roughness in face milling. Journal of Intelligent Manufacturing, 24, 755–762.

    Article  Google Scholar 

  • Khashei, M., & Bijari, M. (2012). Hybridization of the probabilistic neural networks with feed-forward neural networks for forecasting. Engineering Applications of Artificial Intelligence, 25, 1277–1288.

    Article  Google Scholar 

  • Kuo, R. J., & Cohen, P. H. (1998). Manufacturing process control through integration of neural networks and fuzzy model. Fuzzy Sets and Systems, 98(1), 15–31.

    Article  Google Scholar 

  • Kuo, R. J. (2001). A sales forecasting system based on fuzzy neural network with initial weights generated by genetic algorithm. European Journal of Operational Research, 129(3), 496–517.

    Article  Google Scholar 

  • Kuo, R. J., Wu, P. C., & Wang, C. P. (2002). An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight-elimination. Neural Networks, 15(7), 909–925.

    Article  Google Scholar 

  • Kuo, R. J., Hong, S. Y., & Huang, Y. C. (2010). Integration of particle swarm optimization-based fuzzy neural network and artificial neural network for supplier selection. Applied Mathematical Modelling, 34(12), 3976–3990.

    Article  Google Scholar 

  • Kuo, R. J., Tseng, W. L., Tien, F. C., & Liao, W. T. (2012). Application of an artificial immune system-based fuzzy neural network to a RFID-based positioning system. Computers & Industrial Engineering, 63(4), 943–956.

    Article  Google Scholar 

  • Kuo, R. J., & Chang, J. W. (2014). Intelligent RFID positions system through immune-based feed-forward neural network. Journal of Intelligent Manufacturing. doi:10.1007/s10845-013-0832-0.

  • Lin, C. T., & Lee, C. S. G. (1991). Neural-network-based fuzzy logic control and decision system. IEEE Transactions on Computer, 40(12), 1320–1336.

  • Lin, G. F., & Wu, M. C. (2011). An RBF network with a two-step learning algorithm for developing a reservoir inflow forecasting model. Journal of Hydrology, 405, 439–450.

    Article  Google Scholar 

  • Lu, C. J., & Wang, Y. W. (2010). Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting. International Journal of Production Economics, 128, 603–613.

    Article  Google Scholar 

  • Mamdani, E. H. (1974). Application of fuzzy algorithms for control of simple dynamic plant. Proceedings of the Institution of Electrical Engineers, 121(12), 1585–1588.

    Article  Google Scholar 

  • Qasem, S. N., Shamsuddin, S. M., & Zain, A. M. (2012). Multi-objective hybrid evolutionary algorithms for radial basis function neural network design. Knowledge-Based Systems, 27, 475–497.

    Article  Google Scholar 

  • Qiu, X., & Lau, H. Y. K. (2014). An AIS-based hybrid algorithm for static job shop scheduling problem. Journal of Intelligent Manufacturing, 25, 489–503.

    Article  Google Scholar 

  • Roitt, I., & Brostoff, J. (1998). Immunology. New York: Mosby Int. Ltd.

    Google Scholar 

  • Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representation by back propagation errors. Nature, 323, 533–536.

    Article  Google Scholar 

  • Shibata, T., Fukuda, T., Kosuge, T., & Arai, F. (1992). Skill based control by using fuzzy neural network for hierarchical intelligent control. In Proceedings of IJCNN’92 (Vol. 2, pp. 81–86).

  • Taguchi, G., Chowdhury, S., & Wu, Y. (2005). Taguchi’s quality engineering handbook. NJ: Wiley, Hoboken.

    Google Scholar 

  • Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15(1), 116–132.

    Article  Google Scholar 

  • Teimouri, R., & Baseri, H. (2014). Forward and backward predictions of the friction stir welding parameters using fuzzy-artificial bee colony-imperialist competitive algorithm systems. Journal of Intelligent Manufacturing. doi:10.1007/s10845-013-0784-4.

  • Tien, J. P., & Li, T. H. S. (2012). Hybrid Taguchi-chaos of multilevel immune and the artificial bee colony algorithm for parameter identification of chaotic systems. Computers and Mathematics with Applications, 64, 1108–1119.

    Article  Google Scholar 

  • Wei, Y., & Chen, M. C. (2012). Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transportation Research Part C, 21, 148–162.

    Article  Google Scholar 

  • Yudong, Z., & Lenan, W. (2009). Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network. Expert Systems with Applications, 36, 8849–8854.

    Article  Google Scholar 

  • Zadeh, L. A. (1994). Fuzzy logic, neural networks, and soft computing. Communications of the ACM, 37(3), 77–84.

  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.

    Article  Google Scholar 

  • Zhao, H. Q., & Zhang, J. S. (2008). Functional link neural network cascaded with Chebyshev orthogonal polynomial for nonlinear channel equalization. Signal Processing, 88(8), 1946–1957.

    Article  Google Scholar 

  • Zhou, S., Lai, K. K., & Yen, J. (2012). A dynamic meta-learning rate-based model for gold market forecasting. Expert Systems with Applications, 39, 6168–6173.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Management, National Taiwan University of Science and Technology, No. 43, Section 4, Kee-Lung Road, Taipei, 106, Taiwan

    R. J. Kuo

  2. American Megatrends Inc., 11F, No. 2, Sec. 3, Min-Chuan E. Rd., Taipei, 106, Taiwan

    Y. S. Tseng

  3. Department of Business Administration, DE LIN Institute of Technology, No. 1, Ln. 380, Qingyun Rd., Tucheng Dist., New Taipei City, 23654, Taiwan

    Zhen-Yao Chen

Authors
  1. R. J. Kuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Y. S. Tseng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhen-Yao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. J. Kuo.

Appendix

Appendix

See Tables 9, 10 and 11.

Table 9 The questionnaire survey for market experts
Full size table
Table 10 The questionnaire results for FNN
Full size table
Table 11 The mapping rules versus Output\(_{\mathrm{FNN}}\)
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kuo, R.J., Tseng, Y.S. & Chen, ZY. Integration of fuzzy neural network and artificial immune system-based back-propagation neural network for sales forecasting using qualitative and quantitative data. J Intell Manuf 27, 1191–1207 (2016). https://doi.org/10.1007/s10845-014-0944-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-014-0944-1

Keywords

Search

Navigation