Abstract
Adaptive neuro-fuzzy inference system (ANFIS) is efficient estimation model not only among neuro-fuzzy systems but also various other machine learning techniques. Despite acceptance among researchers, ANFIS suffers from limitations that halt applications in problems with large inputs; such as, curse of dimensionality and computational expense. Various approaches have been proposed in literature to overcome such shortcomings, however, there exists a considerable room of improvement. This paper reports approaches from literature that reduce computational complexity by architectural modifications as well as efficient training procedures. Moreover, as potential future directions, this paper also proposes conceptual solutions to the limitations highlighted.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zadeh, L.A.: Fuzzy logica personal perspective. Fuzzy Sets Syst. 281, 4–20 (2015)
Kar, S., Das, S., Ghosh, P.K.: Applications of neuro fuzzy systems: a brief review and future outline. Appl. Soft Comput. 15, 243–259 (2014)
Panella, M.: A hierarchical procedure for the synthesis of anfis networks. Adv. Fuzzy Syst. 2012, 20 (2012)
Zamani, H.A., Rafiee-Taghanaki, S., Karimi, M., Arabloo, M., Dadashi, A.: Implementing anfis for prediction of reservoir oil solution gas-oil ratio. J. Nat. Gas Sci. Eng. 25, 325–334 (2015)
Jang, J.-S.R.: Anfis: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)
Alizadeh, M., Lewis, M., Zarandi, M.H.F., Jolai, F.: Determining significant parameters in the design of anfis. In: Fuzzy Information Processing Society (NAFIPS), 2011 Annual Meeting of the North American, pp. 1–6. IEEE (2011)
Sugeno, M., Tanaka, K.: Successive identification of a fuzzy model and its applications to prediction of a complex system. Fuzzy Sets Syst. 42(3), 315–334 (1991)
Inc., The MathWorks. anfis (2017)
Ciftcioglu, O., Bittermann, M.S., Sariyildiz, I.S.: A neural fuzzy system for soft computing. In: Fuzzy Information Processing Society, NAFIPS 2007, Annual Meeting of the North American, pp. 489–495. IEEE (2007)
Lichman, M.: Uci machine learning repository (2013)
Rini, D.P., Shamsuddin, S.M., Yuhaniz, S.S.: Balanced the trade-offs problem of anfis using particle swarm optimisation. TELKOMNIKA (Telecommun. Comput. Electron. Control) 11(3), 611–616 (2013)
Barati-Harooni, A., Najafi-Marghmaleki, A., Mohammadi, A.H.: Anfis modeling of ionic liquids densities. J. Mol. Liq. 224, 965–975 (2016)
Tatar, A., Barati-Harooni, A., Najafi-Marghmaleki, A., Norouzi-Farimani, B., Mohammadi, A.H.: Predictive model based on anfis for estimation of thermal conductivity of carbon dioxide. J. Mol. Liq. 224, 1266–1274 (2016)
Taylan, O., Karagözoğlu, B.: An adaptive neuro-fuzzy model for prediction of student’s academic performance. Comput. Ind. Eng. 57(3), 732–741 (2009)
Peymanfar, A., Khoei, A., Hadidi, K.: A new anfis based learning algorithm for cmos neuro-fuzzy controllers. In: 14th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2007, pp. 890–893. IEEE (2007)
Orouskhani, M., Mansouri, M., Orouskhani, Y., Teshnehlab, M.: A hybrid method of modified cat swarm optimization and gradient descent algorithm for training anfis. Int. J. Comput. Intell. Appl. 12(02), 1350007 (2013)
Zuo, L., Hou, L., Zhang, W., Geng, S., Wu, W.: Application of PSO-adaptive neural-fuzzy inference system (ANFIS) in analog circuit fault diagnosis. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010. LNCS, vol. 6146, pp. 51–57. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13498-2_7
Karaboga, D., Kaya, E.: An adaptive and hybrid artificial bee colony algorithm (aabc) for anfis training. Appl. Soft Comput. 49, 423–436 (2016)
Soh, A.C., Kean, K.Y.: Reduction of anfis-rules based system through k-map minimization for traffic signal controller. In: 12th International Conference on Control, Automation and Systems 2012 (ICCAS), pp. 1290–1295. IEEE (2012)
Polat, K., Güneş, S.: An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. Digit. Signal Proc. 17(4), 702–710 (2007)
Güneri, A.F., Ertay, T., YüCel, A.: An approach based on anfis input selection and modeling for supplier selection problem. Expert Syst. Appl. 38(12), 14907–14917 (2011)
Acknowledgments
The authors would like to thank Universiti Tun Hussein Onn Malaysia (UTHM), Malaysia for supporting this research under Postgraduate Incentive Research Grant, Vote No.U728.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Salleh, M.N.M., Talpur, N., Hussain, K. (2017). Adaptive Neuro-Fuzzy Inference System: Overview, Strengths, Limitations, and Solutions. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_52
Download citation
DOI: https://doi.org/10.1007/978-3-319-61845-6_52
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61844-9
Online ISBN: 978-3-319-61845-6
eBook Packages: Computer ScienceComputer Science (R0)