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Neuro-Fuzzy and Soft Computing - A Computational Approach to Learning and Artificial Intelligence


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DOI: https://doi.org/10.15866/ireaco.v13i4.19179

Abstract


Single approaches to soft computing have many limitations and disadvantages. Neural network modelling poses a challenge of architecture building, whilst fuzzy sets are characterized by problematic membership functions. The use of hybrid methods is, by contrast, a rather promising strategy. This study aims to develop a new prediction methodology by integrating Neuro-Fuzzy Systems (NFS) with a Neuro-Genetic Approach (NGA). Such a design combines the learning capacity of the neural networks and the ability of fuzzy systems to extract the linguistic knowledge. This proposal is expected to predict the suitability of parameters and models with fewer errors and high accuracy. The performance of this system is improved through the use of genetic algorithm for optimizing the neural network parameters such as the learning rate, network impulse, and the number of membership functions for each input variable. The proposed methodology was tested using electromyographic (EMG) data. The results showed high efficiency (92%) of the proposed hybrid technique.
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Keywords


Artificial Neural Networks; Fuzzy Systems; Genetic Algorithm; Hybrid Method; Soft Computing

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References


N.J. Nilsson, Principles of artificial intelligence (Morgan Kaufmann, 2014).

B.H. Li, B.C. Hou, W.T. Yu, X.B. Lu, C.W. Yang, Applications of artificial intelligence in intelligent manufacturing: a review, Frontiers of Information Technology & Electronic Engineering, Vol. 18(Issue 1): 86-96, 2017.

la Fé-Perdomo, I., Beruvides, G., Quiza, R., Haber, R., & Rivas, M., Automatic selection of optimal parameters based on simple soft-computing methods: a case study of micromilling processes. IEEE Transactions on Industrial Informatics, Vol. 15 (Issue 2): 800-811, 2018.
https://doi.org/10.1109/tii.2018.2816971

L.R. Medsker, Hybrid intelligent systems (Springer Science & Business Media, 2012).

Omolaye, P. O., Mom, J. M., & Igwue, G. A., A Holistic Review of Soft Computing Techniques. Applied and Computational Mathematics, Vol. 6(Issue 2): 93, 2017.

L.A. Zadeh, K.S. Fu, K. Tanaka, Fuzzy sets and their applications to cognitive and decision processes, Proceedings of the Us–Japan Seminar on Fuzzy Sets and Their Applications, Held at the University of California, Berkeley, California (Academic press, 2014).

J.M. Mendel, Uncertain rule-based fuzzy systems, In Introduction and new directions (Springer International Publishing, 2017, p. 684)

M.A. Nielsen, Neural networks and deep learning (Determination press, San Francisco, 2015, Vol. 2018).

P.V.C. Souza, Regularized fuzzy neural networks for pattern classification problems, International Journal of Applied Engineering Research, Vol. 13(Issue 5): 2985-2991, 2018.

Ramírez, E., Melin, P., & Prado-Arechiga, G., Hybrid Model Based on Neural Networks and Fuzzy Logic for 2-Lead Cardiac Arrhythmia Classification. In Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine. Springer, Cham: 193-217, 2020.
https://doi.org/10.1007/978-3-030-34135-0_14

R.S. Zebulum, M.A. Pacheco, M.M.B. Vellasco, Evolutionary electronics: automatic design of electronic circuits and systems by genetic algorithms (CRC press, 2018).
https://doi.org/10.1201/9781420041590

S. Kumar, S. Jain, H. Sharma, Genetic algorithms, In Advances in swarm intelligence for optimizing problems in computer science (Chapman and Hall/CRC, 2018, pp. 27-52).
https://doi.org/10.1201/9780429445927-2

D. Aleksendrić, C. Bellini, P. Carlone, V. Ćirović, F. Rubino, L. Sorrentino, Neural-fuzzy optimization of thick composites curing process, Materials and Manufacturing Processes, 34(Issue 3), 262-273, 2019.
https://doi.org/10.1080/10426914.2018.1512116

D. Felka, J. Brodny, Application of neural-fuzzy system in prediction of methane hazard, In International Conference on Intelligent Systems in Production Engineering and Maintenance (Springer, Cham, 2017, pp. 151-160)
https://doi.org/10.1007/978-3-319-64465-3_15

D.T. Bui, B. Pradhan, H. Nampak, Q.T. Bui, Q.A. Tran, Q.P. Nguyen, Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS, Journal of Hydrology, 540: 317-330, 2016.
https://doi.org/10.1016/j.jhydrol.2016.06.027

I. Mansouri, A. Gholampour, O. Kisi, T. Ozbakkaloglu, Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques, Neural Computing and Applications, Vol. 29(Issue 3): 873-888, 2016.
https://doi.org/10.1007/s00521-016-2492-4

A. Branitskiy, I. Kotenko, Hybridization of computational intelligence methods for attack detection in computer networks. Journal of Computational Science, Vol 23: 145-156, 2017
https://doi.org/10.1016/j.jocs.2016.07.010

X. Huang, X. Zeng, Han, R., & Wang, X. An enhanced hybridized artificial bee colony algorithm for optimization problems. IAES International Journal of Artificial Intelligence, Vol 8(Issue 1): 87, 2019.
https://doi.org/10.11591/ijai.v8.i1.pp87-94

A. Louati, A hybridization of deep learning techniques to predict and control traffic disturbances. Artificial Intelligence Review: 1-30, 2020.

Maroufpoor, S., Maroufpoor, E., Bozorg-Haddad, O., Shiri, J., & Yaseen, Z. M. (2019). Soil moisture simulation using hybrid artificial intelligent model: Hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm. Journal of Hydrology, 575, 544-556.
https://doi.org/10.1016/j.jhydrol.2019.05.045

Matar, M., Mohamed, O., Fault Classification on a Power Transmission Line Using Discrete Wavelet Transform and Artificial Neural Networks, (2019) International Review of Electrical Engineering (IREE), 14 (5), pp. 349-357.
https://doi.org/10.15866/iree.v14i5.17017

Zhalnin, V., Zakharova, A., Uzenkov, D., Vlasov, A., Krivoshein, A., Filin, S., Configuration-Making Algorithm for the Smart Machine Controller Based on the Internet of Things Concept, (2019) International Review of Electrical Engineering (IREE), 14 (5), pp. 375-384.
https://doi.org/10.15866/iree.v14i5.16923

Jabri, M., Helmi, A., Neural Network Based on Artificial Intelligence Solution for a Fast Economic Load Dispatch Using the Hybrid Lagrangian Method, (2019) International Journal on Energy Conversion (IRECON), 7 (5), pp. 181-187.
https://doi.org/10.15866/irecon.v7i5.17794

Tynchenko, V., Milov, A., Tynchenko, V., Bukhtoyarov, V., Kukartsev, V., Intellectualizing the Process of Waveguide Tracks Induction Soldering for Spacecrafts, (2019) International Review of Aerospace Engineering (IREASE), 12 (6), pp. 280-289.
https://doi.org/10.15866/irease.v12i6.16910

Zambrano, D., Salcedo, O., Espitia, M., Modelling and Predicting the Behaviour of a Secondary User in Cognitive Radio Using Artificial Intelligence Techniques, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (4), pp. 348-355.
https://doi.org/10.15866/irecap.v7i4.11824

Anuradha, S., Raghuram, G., Sreenivasa Murthy, K., Gurunath Reddy, B., Fast Transfer of Packets Through DB Routing Using Ant Colony Optimization, (2018) International Journal on Engineering Applications (IREA), 6 (1), pp. 35-41.
https://doi.org/10.15866/irea.v6i1.15144

Cheddadi, Y., Diouri, O., Gaga, A., Errahimi, F., Es-Sbai, N., Design and Simulation of an Accurate Neural Network State-of-Charge Estimator for Lithium Ion Battery Pack, (2017) International Review of Automatic Control (IREACO), 10 (2), pp. 186-192.
https://doi.org/10.15866/ireaco.v10i2.11957

S. Bengio, O. Vinyals, N. Jaitly, N. Shazeer, Scheduled sampling for sequence prediction with recurrent neural networks, In Advances in Neural Information Processing Systems (2015, pp. 1171-1179).

A.A. Hameed, B. Karlik, M.S. Salman, Back-propagation algorithm with variable adaptive momentum, Knowledge-Based Systems, 114: 79-87, 2016.
https://doi.org/10.1016/j.knosys.2016.10.001

S. P. Siregar, A. Wanto, Analysis of Artificial Neural Network Accuracy Using Backpropagation Algorithm In Predicting Process (Forecasting), International Journal of Information System & Technology, Vol. 1(Issue 1): 34-42, 2017.
https://doi.org/10.30645/ijistech.v1i1.4

B. C. Aissa, C. Fatima, Neural Networks Trained with Levenberg-Marquardt-Iterated Extended Kalman Filter for Mobile Robot Trajectory Tracking, Journal of Engineering Science & Technology Review, Vol. 10(Issue 4): 191-198, 2017.
https://doi.org/10.25103/jestr.104.23

W. Li, Y. Yin, Y. Wang, L. Zhu, Y. Xia, Y. Qi, Rail Profile Detection Based on PNP Algorithm, In 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) (IEEE, 2018, pp. 2392-2396)
https://doi.org/10.1109/imcec.2018.8469520

N. Fjodorova, M. Novic, S. Zuperl, K. Venko, Counter-Propagation Artificial Neural Network Models for Prediction of Carcinogenicity of Non-congeneric Chemicals for Regulatory Uses, In Advances in QSAR Modeling (Springer, Cham, 2017, pp. 503-527).
https://doi.org/10.1007/978-3-319-56850-8_14

A. Kannappan, A. Tamilarasi, E.I. Papageorgiou, Analyzing the performance of fuzzy cognitive maps with non-linear hebbian learning algorithm in predicting autistic disorder, Expert Systems with Applications, Vol. 38(Issue 3): 1282-1292, 2011.
https://doi.org/10.1016/j.eswa.2010.06.069

T. Kuremoto, S. Kimura, K. Kobayashi, M. Obayashi, Time series forecasting using a deep belief network with restricted Boltzmann machines, Neurocomputing, Vol. 137: 47-56, 2014.
https://doi.org/10.1016/j.neucom.2013.03.047

D. Ballabio, M. Vasighi, A MATLAB toolbox for Self Organizing Maps and supervised neural network learning strategies, Chemometrics and Intelligent Laboratory Systems, Vol. 118: 24-32, 2012.
https://doi.org/10.1016/j.chemolab.2012.07.005

L.A. Zadeh, Fuzzy sets, Information and Control, Vol. 8: 338-353, 1965.

H.T. Nguyen, C.L. Walker, E.A. Walker, A first course in fuzzy logic (CRC press, 2018).

E.H. Mamdani, S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller, International journal of human-computer studies, Vol. 51(Issue 2): 135-147, 1999.
https://doi.org/10.1006/ijhc.1973.0303

F. Bergadano, V. Cutello, Learning membership functions, In European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty (Springer, Berlin, Heidelberg, 1993, pp. 25-32).
https://doi.org/10.1007/bfb0028178

M. Mitchell, An introduction to genetic algorithms (MIT press, 1998).

R. Malhotra, N. Singh, Y. Singh, Genetic algorithms: Concepts, design for optimization of process controllers, Computer and Information Science, Vol. 4(Issue 2): 39, 2011.
https://doi.org/10.5539/cis.v4n2p39

Chebli, S., Elakkary, A., Sefiani, N., Multi-Objective Genetic Algorithm Optimization Using PID Controller for AQM/TCP Networks, (2017) International Review of Automatic Control (IREACO), 10 (1), pp. 33-39.
https://doi.org/10.15866/ireaco.v10i1.11143

L.D. Chambers, Practical handbook of genetic algorithms: complex coding systems (CRC press, 2019, Vol. 3).

W.B. Langdon, R. Poli, Foundations of genetic programming (Springer Science & Business Media, 2013).

R.S. Zebulum, M.A. Pacheco, M.M.B. Vellasco, Evolutionary electronics: automatic design of electronic circuits and systems by genetic algorithms (CRC press, 2018).
https://doi.org/10.1201/9781420041590

P.C. Jennings, S. Lysgaard, J.S. Hummelshøj, T. Vegge, T. Bligaard, Genetic algorithms for computational materials discovery accelerated by machine learning, NPJ Computational Materials, Vol. 5(Issue 1): 1-6, 2019.
https://doi.org/10.1038/s41524-019-0181-4

Taki El-Deen, A., Mahmoud, A., R. El-Sawi, A., Optimal PID Tuning for DC Motor Speed Controller Based on Genetic Algorithm, (2015) International Review of Automatic Control (IREACO), 8 (1), pp. 80-85.
https://doi.org/10.15866/ireaco.v8i1.4839

W.E. Peterman, ResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms, Methods in Ecology and Evolution, Vol. 9(Issue 6): 1638-1647, 2018.
https://doi.org/10.1111/2041-210x.12984

M. Guerrero, F. G. Montoya, R. Baños, A. Alcayde, C. Gil, Adaptive community detection in complex networks using genetic algorithms, Neurocomputing, Vol. 266: 101-113, 2017.
https://doi.org/10.1016/j.neucom.2017.05.029

S. Kar, S. Das, P.K. Ghosh, Applications of neuro fuzzy systems: A brief review and future outline, Applied Soft Computing, Vol. 15: 243-259, 2014.
https://doi.org/10.1016/j.asoc.2013.10.014

M.B. Gorzalczany, Computational intelligence systems and applications: neuro-fuzzy and fuzzy neural synergisms (Physica, 2012 Vol. 86).

R. Syahputra, I. Soesanti, ANFIS Approach for Distribution Network Reconfiguration, International Journal of Applied Engineering Research, Vol. 12(Issue 18): 7775-7782, 2017.

Y. Tan, C. Shuai, L. Jiao, L. Shen, An adaptive neuro-fuzzy inference system (ANFIS) approach for measuring country sustainability performance, Environmental Impact Assessment Review, Vol. 65: 29-40, 2017.
https://doi.org/10.1016/j.eiar.2017.04.004

Yusuf, L., Magaji, N., Comparison of Fuzzy Logic and GA-PID Controller for Position Control of Inverted Pendulum, (2014) International Review of Automatic Control (IREACO), 7 (4), pp. 380-385.
https://doi.org/10.1109/icastech.2014.7068099

Esmaeilian, A., A New Adaptive Neuro Fuzzy Based Algorithm to Identify Power Swing, (2011) International Review of Automatic Control (IREACO), 4 (5), pp. 749-754.

J.-S.R. Jang, C.-T. Sun, E. Mizutani, Neuro-fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence (Prentice Hall, Upper Saddle River, NJ, 1997).

H. Kitano, Neurogenetic learning: an integrated method of designing and training neural networks using genetic algorithms, Physica D: Nonlinear Phenomena, Vol. 75(Issue 1-3): 225-238, 1994.
https://doi.org/10.1016/0167-2789(94)90285-2

F. Sadikoglu, C. Kavalcioglu, B. Dagman, Electromyogram (EMG) signal detection, classification of EMG signals and diagnosis of neuropathy muscle disease, Procedia computer science, Vol. 120: 422-429, 2017.
https://doi.org/10.1016/j.procs.2017.11.259

J. Too, A.R. Abdullah, T. T. Zawawi, N.M. Saad, H. Musa, Classification of EMG signal based on time domain and frequency domain features, International Journal of Human and Technology Interaction, Vol. 1(Issue 1): 25-30, 2017.

A. Subasi, Classification of EMG signals using combined features and soft computing techniques, Applied soft computing, 12(Issue 8): 2188-2198, 2012.
https://doi.org/10.1016/j.asoc.2012.03.035

K. Kiguchi, T. Tanaka, T. Fukuda, Neuro-fuzzy control of a robotic exoskeleton with EMG signals, IEEE Transactions on fuzzy systems, Vol. 12(Issue 4): 481-490, 2004.
https://doi.org/10.1109/tfuzz.2004.832525

H.B. Xie, T. Guo, S. Bai, S. Dokos, Hybrid soft computing systems for electromyographic signals analysis: a review, Biomedical engineering online, Vol. 13(Issue 1): 8, 2014.
https://doi.org/10.1186/1475-925x-13-8

G. Castellano, C. Castiello, A.M. Fanelli, L. Jain, Evolutionary neuro-fuzzy systems and applications, In Advances in Evolutionary Computing for System Design (Springer, Berlin, Heidelberg, 2007, pp. 11-45).
https://doi.org/10.1007/978-3-540-72377-6_2

C.H. Chou, S.C. Hsieh, C.J. Qiu, Hybrid genetic algorithm and fuzzy clustering for bankruptcy prediction. Applied Soft Computing, Vol. 56: 298-316, 2017.
https://doi.org/10.1016/j.asoc.2017.03.014

M. Vázquez, P. Melin, G. Prado-Arechiga, Hybrid Neural-Fuzzy Modeling and Classification System for Blood Pressure Level Affectation, In Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine (Springer, Cham, 2020, 257-269).
https://doi.org/10.1007/978-3-030-34135-0_18


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