Abstract
Heuristic methods are used to design an adaptive-network-based fuzzy inference system (ANFIS) neuro-fuzzy classifier. The reason is that these classifiers include diverse structures, each of which has a considerable effect on their performance. So, the designer of an ANFIS classifier confronts a high-dimensional solution space and heuristic methods are of high capability in solving such problems (finding the best optimum values of these parameters). Using an efficient method of accurate designing to achieve the best performance is considered as the main challenge in terms of these classifiers. In this paper, an intelligent method based on one of the newest heuristic methods called inclined planes system optimization algorithm (IPO) has been proposed and implemented for the first time so that automatic designing of a neuro-fuzzy classifier is performed. IPO method is inspired by the dynamics of spherical objects’ sliding motion along a set of frictionless inclined planes based on which objects in cooperation with each other move towards the best response to the problem according to Newton’s Second Law and equations of motion. The results obtained from repetitive tests performed on several well-known databases with various numbers of reference classes as well as different feature vector lengths with acceptable and certain complexities indicated capability of the proposed method compared to other heuristic methods for automatic design of a neuro-fuzzy classifier.
Similar content being viewed by others
References
Lee CCC (1990) Fuzzy logic in control systems: fuzzy logic controller.II. IEEE Trans Syst Man Cybern 20(2):404–418. https://doi.org/10.1109/21.52551
Ross TJ (2010) Fuzzy Logic with engineering applications, vol 222, 3rd edn. Tata McGraw-Hill Publishing Company limited, New Delhi. https://doi.org/10.1002/9781119994374
Kar S, Das S, Ghosh PK (2014) Applications of neuro fuzzy systems: a brief review and future outline. Appl Soft Comput J 15:243–259. https://doi.org/10.1016/j.asoc.2013.10.014
Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685. https://doi.org/10.1109/21.256541
Zahiri S-H (2010) Swarm intelligence and fuzzy systems (computer science, technology and applications): Seyed-Hamid Zahiri: March 1, 2011
Aliyari Shoorehdeli M, Teshnehlab M, Sedigh AK (2009) Identification using ANFIS with intelligent hybrid stable learning algorithm approaches. Neural Comput Appl 18(2):157–174. https://doi.org/10.1007/s00521-007-0168-9
Nasiri M, Faez K (2012) Extracting fetal electrocardiogram signal using ANFIS trained by genetic algorithm. In: 2012 International Conference on Biomedical Engineering, ICoBE 2012, pp 197–202
Sarkheyli A, Zain AM, Sharif S (2015) Robust optimization of ANFIS based on a new modified GA. Neurocomputing 166(October):357–366. https://doi.org/10.1016/j.neucom.2015.03.060
Rini DP, Shamsuddin SM, Yuhaniz SS (2016) Particle swarm optimization for ANFIS interpretability and accuracy. Soft Comput 20(1):251–262. https://doi.org/10.1007/s00500-014-1498-z
Karaboga D, Kaya E (2016) An adaptive and hybrid artificial bee colony algorithm (aABC) for ANFIS training. Appl Soft Comput 49:423–436. https://doi.org/10.1016/j.asoc.2016.07.039
Thangavel K, Kaja Mohideen A (2016) Mammogram classification using ANFIS with ant colony optimization based learning. Springer, Singapore, pp 141–152. https://doi.org/10.1007/978-981-10-3274-5_12
Rouhibakhsh K, Darvish H, Sabzgholami H, Goodarzi MS (2018) Application of ANFIS-GA as a novel and accurate tool for estimation of interfacial tension of carbon dioxide and hydrocarbon. Pet Sci Technol 36(15):1143–1149. https://doi.org/10.1080/10916466.2018.1465959
Karaboga D, Kaya E (2018) Training ANFIS by using an adaptive and hybrid artificial bee colony algorithm (aABC) for the identification of nonlinear static systems. Arabian J Sci Eng. https://doi.org/10.1007/s13369-018-3562-y
Baghban A, Adelizadeh M (2018) On the determination of cetane number of hydrocarbons and oxygenates using Adaptive neuro fuzzy inference system optimized with evolutionary algorithms. Fuel 230:344–354. https://doi.org/10.1016/J.FUEL.2018.05.032
Aghel B, Rezaei A, Mohadesi M (2018) Modeling and prediction of water quality parameters using a hybrid particle swarm optimization-neural fuzzy approach. Int J Environ Sci Technol. https://doi.org/10.1007/s13762-018-1896-3
Haznedar B, Kalinli A (2018) Training ANFIS structure using simulated annealing algorithm for dynamic systems identification. Neurocomputing 302:66–74. https://doi.org/10.1016/J.NEUCOM.2018.04.006
Saee AD, Baghban A, Zarei F, Zhang Z, Habibzadeh S (2018) ANFIS based evolutionary concept for estimating nucleate pool boiling heat transfer of refrigerant-ester oil containing nanoparticles. Int J Refrig 96:38–49. https://doi.org/10.1016/J.IJREFRIG.2018.08.002
Semero YK, Zheng D, Zhang J (2018) A PSO-ANFIS based hybrid approach for short term pv power prediction in microgrids. Electr Power Compon Syst 46(1):95–103. https://doi.org/10.1080/15325008.2018.1433733
Mozaffari MH, Abdy H, Zahiri SH (2016) IPO: an inclined planes system optimization algorithm. Comput Inform 35(1):222–240
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings, IEEE international conference on neural networks, vol 1944, no 4, pp 1942–1948. https://doi.org/10.1109/ICNN.1995.488968
Bäck T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, Oxford
Chelouah R, Siarry P (2000) A continuous genetic algorithm designed for the global optimization of multimodal functions. J Heuristics 6(2):191–213. https://doi.org/10.1023/A:1009626110229
Storn R, Price KV (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359
Socha K, Dorigo M (2008) Ant colony optimization for continuous domains. Eur J Oper Res 185(3):1155–1173. https://doi.org/10.1016/j.ejor.2006.06.046
PNRKS Eswari (2008) Ductility performance of HyFRC. Am J Appl Sci 5(9):1257–1262
Bache K, Lichman M (2013) UCI machine learning repository, Univ. Calif. Irvine Sch. Inf. 2008
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Soltany Mahboob, A., Zahiri, S.H. Application of IPO: a heuristic neuro-fuzzy classifier. Evol. Intel. 12, 165–177 (2019). https://doi.org/10.1007/s12065-019-00207-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12065-019-00207-8