Skip to main content

A Neuro-Fuzzy Decision Support System for Selection of Small Scale Business

  • Conference paper

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 81))

Abstract

Artificial Neural Network (ANN) and Fuzzy Logic (FL) are two important and useful technologies having their strengths and weaknesses. The combination of fuzzy logic and neural networks constitutes a powerful means for intelligent system development and offers dual advantages of the technologies. This article describes four approaches of neuro-fuzzy systems with their broad design and also presents general structure of a business advisory system using hybrid neuro-fuzzy approach. The system utilizes ANN that considers basic parameters and data from the environment for selection of a small-scale business in the given area and generates rules accordingly. Finally, the article presents sample rules extracted from the neuro-fuzzy system, screens for the interface design and parameters for implementation.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Zadeh, L.: Fuzzy Logic - Computing with Words. IEEE Transactions on Fuzzy Systems 4(2), 103–111 (1996)

    Article  MathSciNet  Google Scholar 

  2. Nauck, D.: Beyond Neuro-Fuzzy: Perspective and Directions. In: 3rd European Congress on Intelligent Techniques and Soft Computing (EUFIT 1995), Aachen, pp. 1159–1164 (1995)

    Google Scholar 

  3. Sajja, P.S.: A Fuzzy Agent to Input Vague Parameters into Multi-Layer Connectionist Expert System: An Application for Stock Market. ADIT Journal of Engineering 3, 30–32 (2006)

    Google Scholar 

  4. Sajja, P.S.: Fuzzy Artificial Neural Network Decision Support System for Course Selection. Journal of Engineering and Technology 19, 99–102 (2006)

    Google Scholar 

  5. Abraham, A.: Neuro-Fuzzy Systems: State-of-the-Art Modeling Techniques. In: 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence, pp. 269–276 (2001)

    Google Scholar 

  6. Narazaki, H., Ralescu, A.L.: A Synthesis Method for Multi-Layered Neural Network using Fuzzy Sets. In: IJCAI 1991: Workshop on Logic in Artificial Intelligence, Sydney, pp. 54–66 (1991)

    Google Scholar 

  7. Halgamuge, S.K., Glesner, M.: Neural Networks in Designing Fuzzy Systems for Real Word Applications. Fuzzy Sets and Systems 65, 1–12 (1994)

    Article  Google Scholar 

  8. Jang, J.S.: Neuro-Fuzzy Modeling: Architectures, Analyses and Applications, Ph.D Thesis, University of California, Berkeley (1992)

    Google Scholar 

  9. Nauck, D., Kruse, R.: NEFCLASS, A Neuro-Fuzzy Approach for the Classification of Data. In: ACM Symposium on Applied Computing, Nashville, pp. 461–465 (1995)

    Google Scholar 

  10. Tschichold-Gurman, N.: Generation and Improvement of Fuzzy Classifiers with Incremental Learning using Fuzzy Rulenet. In: ACM Symposium on Applied Computing, Nashville, pp. 466–470 (1995)

    Google Scholar 

  11. Rutkowski, L., Cpałka, K.: Flexible Neuro-Fuzzy Systems. IEEE Transactions on Neural Networks 14(1), 554–574 (2003)

    Article  Google Scholar 

  12. Sajja, P.S.: Type-2 Fuzzy user Interface for Artificial Neural Network-Based Decision Support System for Course Selection. International Journal of Computing and ICT Research 2(2), 96–102 (2008)

    Google Scholar 

  13. Jang, J.S.: ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man and Cybernetics 23(3), 665–685 (1993)

    Article  MathSciNet  Google Scholar 

  14. Takagi, H.: Fusion Technology of Fuzzy Theory and Neural Networks: Survey and Future Directions. In: International Conference on Fuzzy Logic & Neural Networks, Iizuka, Japan, pp. 13–26 (1990)

    Google Scholar 

  15. Mitra, S., Hayashi, Y.: Neuro-Fuzzy Rule Generation: Survey in Soft Computing Framework. IEEE Transactions on Neural Networks 11(3), 748–768 (2000)

    Article  Google Scholar 

  16. Caponetti, L., Castiello, C., Górecki, P.: Document Page Segmentation using Neuro-Fuzzy Approach. Applied Soft Computing 8(1), 118–126 (2008)

    Article  Google Scholar 

  17. Cho, S., Quek, C., Seah, S., Chong, C.: HebbR2-Taffic: A Novel Application of Neuro-Fuzzy Network for Visual Based Traffic Monitoring System. Expert System Application 36(3), 6343–6356 (2009)

    Article  Google Scholar 

  18. Sajja, P.S.: Type-2 Fuzzy Interface for Artificial Neural Network. In: Anbumani, K., Nedunchezhian, R. (eds.) Soft Computing Applications in Database Technology: Techniques and Issues. IGI Global Book Publishing, Hershey (2010)

    Google Scholar 

  19. Jian, X.L., Hui, H.S.: Developing Soft Sensor using Hybrid Soft Computing Methodology: A Neuro-Fuzzy System Based on Rough Set Theory and Genetic Algorithms. Soft Computing 10(1), 54–60 (2006)

    Article  Google Scholar 

  20. Jun, L., Ding, L., Hua, Y., Sheng, W., Xia, H.: A New Strategy for Optimizing the Parameters Updating Algorithm of Fuzzy Neural Controller. Soft Computing - A Fusion of Foundations, Methodologies and Applications 10(1), 61–67 (2006)

    Google Scholar 

  21. Negnevitsky, M.: Artificial Intelligence: A Guide to Intelligent Systems. Pearson Education Limited, England (2002)

    Google Scholar 

  22. Kasabov, N.: NeuCom - A Neuro-Computing Decision Support Environment (2007), http://www.aut.ac.nz/research/research_institutes/kedri/research_centres/centrefor_data_mining_and_decision_support_systems/neucom.htm

  23. Sajja, P.S.: Knowledge-Based Systems for Socio-Economic Rural Development. Ph.D Thesis, Sardar Patel University, India (2000)

    Google Scholar 

  24. Sajja, P.S.: Multi-Layer Connectionist Model of Expert System for an Advisory System. In: National Level Seminar - Tech. Symposia on IT Futura, Anand, India (2006)

    Google Scholar 

  25. Mankad, K.B., Sajja, P.S.: A Design of Encoding Strategy and Fitness Function for Genetic-Fuzzy System for Classification of Student Skills. In: International Conference on Signals, Systems and Automation (ICSSA 2009), Vallabh Vidyanagar, India (2009)

    Google Scholar 

  26. Dixon, B.: Prediction of Ground Water Vulnerability using an Integrated GIS-Based Neuro-Fuzzy Techniques. Journal of Spatial Hydrology 4(2), 1–38 (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Akerkar, R., Sajja, P.S. (2010). A Neuro-Fuzzy Decision Support System for Selection of Small Scale Business. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2010. Communications in Computer and Information Science, vol 81. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14058-7_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-14058-7_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14057-0

  • Online ISBN: 978-3-642-14058-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics