Training Anfis System with Moth-Flame Optimization Algorithm
DOI:
https://doi.org/10.18201/ijisae.2019355375Keywords:
ANFIS, classification, machine learning, moth-flame optimization, neural computingAbstract
Adaptive Neuro Fuzzy Inference System (ANFIS) is an adaptive network that can use the computation and learning abilities of artificial neural network together with the inference feature of fuzzy logic. The ANFIS system, which is used in the solution of many problems such as classification and estimation of deep learning applications, meets the needs in many different areas such as modeling, control, and parameter estimation. In recent years, heuristic methods have been used for the training of this network, which requires initial and result parameters by its structure. Moth-Flame Optimization Algorithm (MFO) is one of the current heuristic methods modeled by the influence of the spiral movement of the moths towards the light source. In this study, the MFO algorithm was used for the first time for the optimization of initial and result parameters in the ANFIS system. In the determination of parameters, nonlinear system identification, time series estimation, classification problems were tried to be solved. When the results obtained for the ANFIS trained with the known heuristic methods such as Particle Swarm Optimization(PSO), Genetic Algorithm(GA) and Whale Optimization Algorithm(WOA) and the results of ANFIS trained by the MFO were examined, it was observed that the MFO had lower error values.
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References
Zadeh L.A., “Fuzzy Sets”. Elsevier Information and Control, 8, 338-35,1965.
Şen Z., Bulanık Mantık İlkeleri ve Modelleme, Genişletilmiş 3. Baskı, Su Vakfı Yayınları, 2009.
Mirjalili, S., Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm, Knowledge-Based Systems,Volume 89, 2015,Pages 228-249,
Kennedy, J., Eberhart, R. "Particle Swarm Optimization". Proceedings of IEEE International Conference on Neural Networks. IV. pp. 1942–1948, 1995.
Winter, G., Periaux J., Galan, M., Genetic Algorithms in Engineering and Computer Science , John Wiley & Son Ltd. 1995.
Mirjalili, S., Lewis, A., The Whale Optimization Algorithm,Advances in Engineering Software,Volume 95,2016,Pages 51-67,
Yamany, W., et al. "Moth-flame optimization for training multi-layer perceptrons." Computer Engineering Conference (ICENCO), 2015 11th International. IEEE, 2015.
Aboul Ella, H., et al. "An improved moth flame optimization algorithm based on rough sets for tomato diseases detection." Computers and Electronics in Agriculture 136 (2017): 86-96
Sayed, G. I., Mona S., Aboul Ella H., "Bio-inspired Swarm Techniques for Thermogram Breast Cancer Detection." Medical Imaging in Clinical Applications. Springer International Publishing, 2016. 487-506.
El Aziz, M.A., Ewees, A. A., and Aboul Ella H., "Whale Optimization Algorithm and Moth-Flame Optimization for Multilevel Thresholding Image Segmentation." Expert Systems with Applications 2017.
Zawbaa, H. M., et al. "Feature selection approach based on moth-flame optimization algorithm." Evolutionary Computation (CEC), 2016 IEEE Congress on. IEEE, 2016.
Kumar, L.D., Bhoi, K.K., Barisal A.K., "Performance evaluation of MFO algorithm for AGC of a multi area power system." transfer 1: 4. 2016
Narottam, J., et al. "Price Penalty factors Based Approach for Combined Economic Emission Dispatch Problem Solution using Moth-flame Optimizer Algorithm",2016
Sayed, G.I., Hassanien, A.E. Complex Intell. Syst. A hybrid SA-MFO algorithm for function optimization and engineering design problems, 2018. https://doi.org/10.1007/s40747-018-0066-z,
Catalao, J. P. S., Pousinho H. M. I.,. Mendes, V. M. F., "Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting", IEEE Transactions on Power Systems, vol. 26, no. 1, pp. 137-144, 2011.
Jiang, H. , Kwong, C. K., Ip W. H., and Wong, T. C.. "Modeling customer satisfaction for new product development using a PSO-based ANFIS approach", Applied Soft Computing, vol. 12, no. 2, pp. 726-734, 2012
Pousinho, H. M. I., Mendes V. M. F., Catalão, J. P. S., "A hybrid PSO–ANFIS approach for short-term wind power prediction in Portugal", Energy Conversion and Management, vol. 52, no. 1, pp. 397-402,2011.
Pousinho, H. M. I., Mendes V. M. F., Catalão, J. P. S., "Short-term electricity prices forecasting in a competitive market by a hybrid PSO–ANFIS approach", International Journal of Electrical Power & Energy Systems, vol. 39, no. 1, pp. 29-35, 2012.
Salleh, M.N.M., Hussain, K., A Review of Training Methods of ANFIS for Applications in Business and Economics, International Journal of u- and e- Service, Science and Technology,Vol.9, No. 7 ,pp.165-172, 2016.
Turki, M., Bouzaida, S., Sakly A., M'Sahli, F., "Adaptive control of nonlinear system using neuro-fuzzy learning by PSO algorithm. in Electrotechnical Conference (MELECON)", 2012 16th IEEE Mediterranean, 2012.
Haznedar, B., Arslan, M.T., Kalınlı, A., Karaciğer mikrodizi kanser verisinin sınıflandırılması için genetik algoritma kullanarak ANFIS’in eğitilmesi, Sakarya Üniversitesi Fen bilimleri Enstitüsü Dergisi, doi: 10.16984/saufenbilder.41925
] Haznedar, B., Kalınlı, A., “Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification,” Int. J. of Intell. Sys. and Appl. in Eng., vol. 4, no. 1, pp. 44-47, 2016.
Karaboga, D., Kaya, E., “Training ANFIS using artificial bee colony algorithm for nonlinear dynamic systems identification,” in 2014 22nd Signal Processing and Communications Applications Conference (SIU), pp. 493–496.
Karaboga, D., Kaya, E., Training ANFIS by using the artificial bee colony algorithm, Turk J Elec Eng & Comp Sci (2017) 25: 1669 – 1679
Thangavel K., Kaja Mohideen A, Mammogram Classification Using ANFIS with Ant Colony Optimization Based Learning. Communications in Computer and Information Science, vol 679. Springer, Singapore, 2016.
Canayaz M., Özdağ R., "Training ANFIS using The Whale Optimization Algorithm ", International Conference on Advanced Technologies, Computer Engineering and Science (ICATCES 2018), KARABÜK, TÜRKIYE, 11-13 Mayıs 2018, pp.409-414
Sayed, G. I., et al. "Alzheimer’s Disease Diagnosis Based on Moth Flame Optimization." International Conference on Genetic and Evolutionary Computing. Springer International Publishing, 2016.
Ceylan, O., "Harmonic elimination of multilevel inverters by moth-flame optimization algorithm," 2016 International Symposium on Industrial Electronics (INDEL), Banja Luka, 2016, pp. 1-5.
Jang, J. S. R., “ANFIS: Adaptive-Network-Based Fuzzy Inference System,” IEEE Trans Syst Man Cybern, vol. 23, no. 3, pp. 665–685, 1993.
Jang, J. S. R., Sun, C.T., Mizutani, E., Neurofuzzy and soft computing, Prentice Hall, Upper Saddle River, 1997
Elmas, Ç., Yapay Zeka Uygulamaları, Seçkin Yayıncılık, 2016.
K S, Elhoseny M, S K L, et al. Optimal feature level fusion based ANFIS classifier for brain MRI image classification.Concurrency Computat Pract Exper. 2018;e4887. https://doi.org/10.1002/cpe.4887
Hamam, A., Georganas, N. D., A Comparison of Mamdani and Sugeno Fuzzy Inference Systems for Evaluating the Quality of Experience of Hapto-Audio-Visual Applications, HAVE 2008 – IEEE International Workshop on Haptic Audio Visual Environments and their Applications, Ottawa – Canada, 18-19 October 2008
Vaidhehi, V., The role of Dataset in training ANFIS System for Course Advisor, International Journal of Innovative Research in Advanced Engineering (IJIRAE) ,Volume 1 Issue 6 ,July 2014,pp 249-253
Lin X., Sun J., Palade V., Fang W., Wu X., Xu W. (2012) Training ANFIS Parameters with a Quantum-behaved Particle Swarm Optimization Algorithm. In: Tan Y., Shi Y., Ji Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7331. Springer, Berlin, Heidelberg
Lyon, R. J., Stappers, B. W., Cooper, S., Brooke, J. M. ,. Knowles, J. D., Fifty Years of Pulsar Candidate Selection: From simple filters to a new principled real-time classification approach, Monthly Notices of the Royal Astronomical Society 459 (1), 1104-1123, DOI: 10.1093/mnras/stw656
Volker L., Helene D., UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. 2013.
Mangasarian O. L., Wolberg, W. H.. "Cancer diagnosis via linear programming", SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18
Mackey, M.C., Glass, L., Oscillation and chaos in physiological control systems. Science, 197 (1977), pp. 287-289
PAMUK, N., Dinamik Sistemlerde Kaotik Zaman Dizilerinin Tespiti, BAÜ Fen Bil. Enst. Dergisi Cilt 15(1) 77-91 (2013)
Weeks, E.R., My Adventures in Chaotic Time Series Analysis,2018 http://www.physics.emory.edu/faculty/weeks//research/tseries1.html
Rössler, O. E., Chaotic behavior in simple reaction system, Zeitschrift für Naturforsch A, 31, 259-264, 1976.
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