CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey

by Navneet Walia, Harsukhpreet Singh, Anurag Sharma
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 123 - Number 13
Year of Publication: 2015
Authors: Navneet Walia, Harsukhpreet Singh, Anurag Sharma
10.5120/ijca2015905635

Navneet Walia, Harsukhpreet Singh, Anurag Sharma . ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey. International Journal of Computer Applications. 123, 13 ( August 2015), 32-38. DOI=10.5120/ijca2015905635

@article{ 10.5120/ijca2015905635,
author = { Navneet Walia, Harsukhpreet Singh, Anurag Sharma },
title = { ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 123 },
number = { 13 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 32-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume123/number13/22020-2015905635/ },
doi = { 10.5120/ijca2015905635 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:12:38.048917+05:30
%A Navneet Walia
%A Harsukhpreet Singh
%A Anurag Sharma
%T ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 123
%N 13
%P 32-38
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we presented the architecture and basic learning process underlying ANFIS (adaptive-network-based fuzzy inference system) which is a fuzzy inference system implemented in the framework of adaptive networks. Soft computing approaches including artificial neural networks and fuzzy inference have been used widely to model expert behavior. Using given input/output data values, the proposed ANFIS can construct mapping based on both human knowledge (in the form of fuzzy if-then rules) and hybrid learning algorithm. In modeling and simulation, the ANFIS strategy is employed to model nonlinear functions, to control one of the most important parameters of the induction machine and predict a chaotic time series, all yielding more effective, faster response or settling times.

References
  1. Jang R., “ANFIS :Adaptive-Network-Based Fuzzy Inference System,” IEEE Transactions on Systems, MAN, and Cybernetics, Vol. 23, No. 3, 1993.
  2. Hamdan H., Garibaldi M., “Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Breast Cancer Survival,” WCCI 2010, IEEE World Congress on Computational Intelligence, pp. 18–23, 2010.
  3. Prasath V., Lakshmi N., Nathiya M., Bharathan N., Neetha P., “A Survey on the Applications of Fuzzy Logic in Medical Diagnosis Support Systems Systems Decision,” International Journal of Scientific & Engineering Research, ISSN 2229-5518, Vol. 4, No. 4, pp. 1199–1203, 2013.
  4. Qiu M., “Data Mining Based on Compensation Fuzzy Neural Networks,” International Conference on Education, Management, Commerce and Society, International Conference on Education, Management, Commerce and Society, pp. 306–311, 2015.
  5. Mousavi J., Ponnambalam K., Karray F., “Inferring operating rules for reservoir operations using fuzzy regression and ANFIS,” Fuzzy Sets and Systems, Elsevier, vol. 158, pp. 1064–1082, 2007.
  6. Jang R., “Neuro-Fuzzy Modeling,” proceedings of the IEEE, vol. 83, no. 3, 1995.
  7. Taylan O., “An adaptive neuro-fuzzy model for prediction of student’s academic performance,” Computers & Industrial Engineering, Elsevier, vol. 57, pp. 732-741, 2009.
  8. Rezaei K., Hosseini R., Mazinani M., “A Fuzzy Inference System for Assessment of the Severity of the peptic ulcers,” Computer Science & Information Technology, DOI:10.5121/ csit.2014.4527, pp. 263-271, 2014.
  9. Kaur A., Kaur A., “Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Air Conditioning System,” International Journal of Soft Computing and Engineering, ISSN: 2231-2307, no. 2, pp. 323-325, 2012.
  10. Efosa C., Akwukwuma N., “Knowledge based Fuzzy Inference System for Sepsis Diagnosis,” International Journal of Computational Science and Information Technology, Vol.1, No. 3, pp. 1-7, 2013.
  11. Roy S., “Design of adaptive neuro-fuzzy inference system for predicting surface roughness in turning operation,” Journal of Scientific & Industrial Research, vol. 64, no. September, pp. 653-659, 2005.
  12. Choi H., Yoo H., Jung H., Lim T., Lee K., Ahn K., “An ANFIS-based Energy Management Inference Algorithm with Scheduling Technique for Legacy Device,” International Conference on Artificial Intelligence, Energy and Manufacturing Engineering, pp. 80-82, 2015.
  13. Uc T., Karahoca A., Karahoca D., “Tuberculosis disease diagnosis by using adaptive neuro-fuzzy inference system and rough sets,” Neural Comput & Applications, Springer, DOI 10.1007/s00521-012-0942-1, pp. 471-483, 2013.
  14. Neshat M., Adeli A., “A Comparative Study on ANFIS and Fuzzy Expert System Models for Concrete Mix Design,” International Journal of Computer Science Issues, ISSN 1694-0814, Vol. 8, No. 3, pp. 196-210, 2011.
  15. S. Mitra, Y. Hayashi, S. Member, “Neuro Fuzzy Rule Generation : Survey in,” IEEE transactions on neural networks, Vol. 11, No. 3, pp. 748-768, 2000.
  16. Semary A., Edmonds J., “A Framework for Hybrid Fuzzy Logic Intrusion Detection Systems,” IEEE International Conference on Fuzzy Systems, pp. 325-330, 2005.
  17. Pratama M., Rajab S., Joo E., “Extended Approach of ANFIS in Cascade Control,” International Journal of Computer and Electrical Engineering, Vol. 3, No. 4, pp. 1-5, 2011.
  18. Patel D., Parekh F., “Flood Forecasting using Adaptive Neuro-Fuzzy Inference System,” International Journal of Engineering Trends and Technology, ISSN: 2231-5381, vol. 12, no. 10, pp. 510-514, 2014.
  19. Power E., “Adaptive Neuro Fuzzy Inference System (ANFIS) For Fault Classification in the Transmission Lines,” Online Journal on Electronics and Electrical Engineering, Ref: W09-0030, no. 2, pp. 164-169.
  20. Jhin C., Hwang T., “Prediction of Radical Scavenging Activities of Anthocyanins Applying Adaptive Neuro-Fuzzy Inference System(ANFIS) with Quantum Chemical Descriptors,” International Journal of Molecular Science, DOI: 10.3390/ijms150814715, pp. 14715-14727, 2014.
  21. Gite A., Bodade M., Raut M., “ANFIS Controller and its Application,” International Journal of Engineering Research & Technology, Vol. 2, No. 2, pp. 1-5, 2013.
  22. Abu-rub H., Iqbal A., Ahmed M., Peng Z., Li Y., Baoming G., “Quasi-Z-Source Inverter-Based Photovoltaic Generation System With Maximum Power Tracking Control Using ANFIS,” IEEE Transactions on Sustainable Energy, Vol. 4, No. 1, pp. 11–20, 2013.
  23. Loganathan C., Girija V, “Investigations on Hybrid Learning in ANFIS,” International Journal of Engineering Research & Applications, Vol. 4, No. 10, pp. 31-37, 2014.
  24. Toosi N., Kahani M., “A Novel Soft Computing Model Using Adaptive Neuro-Fuzzy Inference System for Intrusion Detection,” IEEE International Conference on Networking, Sensing and Control, pp. 15-17, 2007.
  25. Lavanya K., Durai S., Sriman C., Iyengar N., “Fuzzy Rule Based Inference System for Detection and Diagnosis of Lung Cancer,” International Journal of Latest Trends in Computing, Vol. 2, No. 1, pp. 165-171, 2011.
  26. Guillaume S., “Designing Fuzzy Inference Systems from Data an Interpretability-Oriented Review ,” IEEE transactions on fuzzy systems, Vol. 9, No. 3, pp. 426-443, 2001.
  27. Yardimci A., “Soft computing in medicine,” Applied Soft Computing, Elsevier, vol. 9, pp. 1029-1043, 2009.
  28. Walia N., Tiwari K., Malhotra R., “Design and Identification of Tuberculosis using Fuzzy Based Decision Support System,” Advances in Computer Science and Information Technology, ISSN: 2393-9915, vol. 2, no. 8, pp. 57-62, 2015.
  29. Kwong C., Chuah C., Lee W., “Adaptive Network Fuzzy Inference System Handoff Algorithm,” International Journal of Network and Mobile Technologies, ISSN 1832-6758, vol. 1, no. 2, pp. 54–59, 2010.
  30. Obi C., A. Imainvan A., “Decision Support System for the Intelligient Identification of Alzheimer using Neuro Fuzzy logic,” International Journal on Soft Computing, DOI : 10.5121/ijsc.2011.2203, vol. 2, no. 2, pp. 25-38, 2011.
Index Terms

Computer Science
Information Sciences

Keywords

ANFIS Fuzzy Logic Takagi-Sugeno (T-S) Model Learning Algorithm

Powered by PhDFocusTM