Design and Development of Fuzzy Based Complex Machine Learning Models: Two Soft Computing Based Approaches

Design and Development of Fuzzy Based Complex Machine Learning Models: Two Soft Computing Based Approaches
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : Ramandeep Kaur, Shakti Kumar, Amar Singh
DOI : 10.14445/22315381/IJETT-V70I7P238

How to Cite?

Ramandeep Kaur, Shakti Kumar, Amar Singh, "Design and Development of Fuzzy Based Complex Machine Learning Models: Two Soft Computing Based Approaches" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 366-376, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P238

Abstract
This paper discusses designing and developing a TSK type-0 fuzzy logic-based machine learning model using two metaheuristic approaches. The optimized model evolved from the available numerical data. Two recent soft computing-based search and optimization algorithms, namely three-parent genetic algorithm (3PGA) and parallel three-parent genetic algorithm (P3PGA), have been used in the proposed approaches to deal with higher complexities and nonlinearities efficiently. The proposed approaches work in three phases. In the first phase, the proposed approaches evolve the model structure of a fuzzy system. The second phase optimizes the parameters of the fuzzy system with the help of MSE (Mean Squared Error). In the third phase, the code generation of the optimized machine learning model was done for testing purposes. The proposed approaches are tested on a rapid battery charger dataset. These approaches are compared with manually evolved machine learning approaches like KNN, ANN, Multi Regression, and SVR. The proposed approaches successfully evolved, optimized, and implemented the model into a working program. It was observed that P3PGA based approach completely outperforms other machine learning-based approaches by a wider margin. Once evolved and tested, models, can be physically realized in hardware if needed.

Keywords
Asymmetric Searchable Encryption, Blockchain, Backward and Forward privacy, Fuzzy Keyword.

Reference
[1] Li-Xin Wang and Jerry M. Mendel, "Generating Fuzzy rules from examples," IEEE transactions on Systems, Man and Cybernetics, Vol. 22, No.6, pp 1414-1427,1992.
[2] Jerry M. Mendel, George C Mouzouris, "Designing fuzzy logic systems," IEEE transactions on Circuits and Systems II, Analog and Digital Signal Processing, Vol. 44, No.11, pp 1414-1427, 1997
[3] Andreas Bastians, "Identifying fuzzy models utilizing genetic programming," Fuzzy Sets and Systems, 113, (2000) pp 333-350
[4] M. Setnes, R Babuska and H B Verbruggen, "Rule-based modeling: precision and transparency," IEEE transactions on Systems, Man and Cybernetics, Vol. 28, No.1, pp 165-169, 1998.
[5] Shiego Abe, Ming Shong Lan, "Fuzzy rule extraction directly from numerical data for function approximation," IEEE transactions on Systems, Man and Cybernetics, Vol. 25, No.1, pp 119-129,1995.
[6] Sugeno, M. and Kang, G.T., "Structure identification of fuzzy model," Fuzzy Sets and Systems, pp. 15-33, 1988.
[7] Takagi, T. and Sugeno, M. , "Fuzzy identification of systems and its applications to modeling and control" IEEE Transactions on Systems, Man and Cybernetics, 116-132,1985.
[8] Homaifar, A. and Mc. Cormick, E., "Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms," IEEE Transactions on Fuzzy Systems, vol.3, no.2, 129-139,1995.
[9] Chia-Feng Juang, "Combination of Online clustering and Q-value based G.A. for reinforcement fuzzy system design," IEEE transactions on Fuzzy Systems, Vol. 13, No.3, pp 289-302,2005.
[10] Parvinder K., Shakti Kumar, Amarpartap Singh, "Nature Inspired Approaches for Identification of Optimized Fuzzy Model: A Comparative Study," Journal of Multiple Valued Logic & Soft Computing, Vol. 25, pp 555-587, 2015.
[11] Shakti Kumar, K.K. Aggarwal, Arun Khosla, "Fuzzy Modelling with Emphasis on Analog Hardware Implementation - Part I: Design" WSEAS TRANSACTIONS on SYSTEMS, vol.3, no.3, pp.1066-1074, 2004.
[12] Arun Khosla, Shakti Kumar and K.K. Aggarwal, "IDENTIFICATION OF STRATEGY PARAMETERS FOR PARTICLE SWARM OPTIMIZER THROUGH TAGUCHI METHOD", Journal of Zhejiang University Science A; An International Applied Physics and Engineering Journal, Vol. 7, No. 12, 2006.
[13] Shakti Kumar, S.S Walia, A. Kalanidhi, "Fuzzy model identification: a new parallel BB-BC optimization based approach", International Journal of Electronics and Communication Engineering (IJECE) ISSN (P) 2278-9901, pp. 167-178.
[14] Amar Singh, Shakti Kumar, Olaf Simanski, Ajay Singh, "Evolving a Fuzzy Logic Based Electronic Insulin Delivery Model: Three Soft Computing Based Approaches," IEEE Journal of Biomedical and Health Informatics (Communicated).
[15] Fister, I., Yang, X., Brest, J., & Fister, D, “ A Brief Review of Nature-Inspired Algorithms for Optimization, “ ELEKTROTEHNISKI VESTNIK , vol. 80, no.3, pp. 116–122, 2013.
[16] Amar Singh, Ajay Singh, Sukhbir Singh Walia, Shakti Kumar, "Three-Parent GA: A Global Optimization Algorithm and its Applications to Routing in WMNs," Journal of Multiple Valued Logic & Soft Computing, Vol. 32, pp. 407–423, 2019.
[17] Amar Singh, Shakti Kumar, Ajay Singh & Sukhbir S. Walia, P3PGA: Multipopulation 3 Parent Genetic Algorithm and its Application to Routing in WMNs, Implementations and Applications of Machine Learning Editor(s) Prof. Saad Subair and Dr. Christopher Thron, Springer International Publishing AG, pp. 1-28.
[18] Holland J.H., Adaptation in Natural and Artificial Systems, Ph.D. thesis, 1975, University of Michigan Press, Ann Arbor, MI , 1975.
[19] Shakti Kumar, Arun Khosla & KK Aggarwal, "Fuzzy Modeling with Emphasis on Analog Hardware Implementation: Part I," WSEAS Transactions on Systems, Vol. 3, no.3, pp. 1066-1074, 2004.
[20] Anh, Ho Pham Huy, and Cao Van Kien, “Hybrid fuzzy sliding mode control for uncertain PAM robot arm plant enhanced with evolutionary technique,” International Journal of Computational Intelligence Systems, vol. 14, no. 1, pp. 594-604, 2021.
[21] Ge, Dongjiao, and Xiao-Jun Zeng, “Functional Fuzzy System: A Nonlinear Regression Model and Its Learning Algorithm for Functionon-Function Regression,” IEEE Transactions on Fuzzy Systems, 2021.
[22] Kalibatienė, Diana, and Jolanta Miliauskaitė, “A Hybrid Systematic Review Approach on Complexity Issues in Data-Driven Fuzzy Inference Systems Development,” Informatica, vol. 32, no. 1 , pp.85-118, 2021.
[23] Huang, Fei, Alexandre Sava, Kondo H. Adjallah, and Zhouhang Wang, “Fuzzy model identification based on mixture distribution analysis for bearings remaining useful life estimation using small training data set,” Mechanical Systems and Signal Processing, vol.148, pp.107173, 2021.
[24] Wang, Xiao-li, Wei-xin Xie, and Liang-qun Li, “Interacting T.S. fuzzy particle filter algorithm for transfer probability matrix of adaptive online estimation model,” Digital Signal Processing, vol.110, pp.102944, 2021.
[25] Hussain, Walayat, Jose M. Merigó, Muhammad Raheel Raza, and Honghao Gao, “A New QoS Prediction Model using Hybrid IOWAANFIS with Fuzzy C-Means, Subtractive Clustering and Grid Partitioning,” Information Sciences, 2021.
[26] Kaur, Ramandeep, Amar Singh, and Shakti Kumar, “Medical Diagnosis: Implementation of Different Machine Learning Based Approaches,” In 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), IEEE, pp. 1-5, 2021.
[27] Kaur, R., Singh, A. and Singla, J, "Integration of NIC algorithms and ANN: A review of different approaches," In 2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM), . IEEE, pp. 185-190, 2021.
[28] Kaur RA, Singh A, "Fuzzy logic: an overview of different application areas," Advances and Applications in Mathematical Sciences. Vol.18, no.8, pp.677-89, 2019.