Implementation of Takagi Sugeno Kang Fuzzy with Rough Set Theory and Mini-Batch Gradient Descent Uniform Regularization
Abstract
Doi: 10.28991/ESJ-2023-07-03-09
Full Text: PDF
Keywords
References
Cui, Y., Wu, D., & Huang, J. (2020). Optimize TSK Fuzzy Systems for Classification Problems: Minibatch Gradient Descent with Uniform Regularization and Batch Normalization. IEEE Transactions on Fuzzy Systems, 28(12), 3065–3075. doi:10.1109/TFUZZ.2020.2967282.
Suryatini, F., Maimunah, M., & Fauzandi, F. I. (2019). Implementation of a Drip Irrigation Control System Using the IoT Concept Based on Fuzzy Takagi-Sugeno Logic. JTERA (Jurnal Teknologi Rekayasa), 4(1), 115. doi:10.31544/jtera.v4.i1.2019.115-124.
Muhajirah, A., Safitri, E., Mardiana, T., Hartina, H., & Setiawan, A. (2019). Analysis of the Accuracy Level of the Neuro Fuzzy Method in Predicting HDI Data in NTB. JTAM | Journal of Mathematical Theory and Application, 3(1), 58. doi:10.31764/jtam.v3i1.769. (In Indonesian).
Hajar, S., Badawi, M., Setiawan, Y. D., Siregar, M. N. H., & Windarto, A. P. (2020). Calculation Prediction of Mahanda Tofu Production with Fuzzy Sugeno Technique. J-SAKTI (Journal of Computer Science and Informatics), 4(1), 210-219. doi:10.30645/j-sakti.v4i1.200. (In Indonesian).
Hu, Z., Wang, J., Zhang, C., Luo, Z., Luo, X., Xiao, L., & Shi, J. (2022). Uncertainty Modeling for Multicenter Autism Spectrum Disorder Classification Using Takagi-Sugeno-Kang Fuzzy Systems. IEEE Transactions on Cognitive and Developmental Systems, 14(2), 730–739. doi:10.1109/TCDS.2021.3073368.
Shaheen, O., El-Nagar, A. M., El-Bardini, M., & El-Rabaie, N. M. (2020). Stable adaptive probabilistic Takagi–Sugeno–Kang fuzzy controller for dynamic systems with uncertainties. ISA Transactions, 98, 271–283. Doi:10.1016/j.isatra.2019.08.035.
Du, A., Shi, X., Guo, X., Pei, Q., Ding, Y., Zhou, W., Lu, Q., & Shi, H. (2021). Assessing the Adequacy of Hemodialysis Patients via the Graph-Based Takagi-Sugeno-Kang Fuzzy System. Computational and Mathematical Methods in Medicine, 2021. doi:10.1155/2021/9036322.
Pan, J., Kang, Z., Zhang, W., Zhang, B., & Zhang, P. (2020). A Delay-dependent Glucose Control Approach for Type-1 Diabetes via Takagi-Sugeno Fuzzy Models. 2020 Chinese Control and Decision Conference (CCDC). doi:10.1109/ccdc49329.2020.9163827.
Buele, J., Ríos-Cando, P., Brito, G., Moreno-P., R., Salazar, F.W. (2020). Temperature Controller Using the Takagi-Sugeno-Kang Fuzzy Inference System for an Industrial Heat Treatment Furnace. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020, Lecture Notes in Computer Science, 12254, Springer, Cham, Switzerland. doi:10.1007/978-3-030-58817-5_27.
Bisong, E. (2019). Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress, Berkeley, United States. doi:10.1007/978-1-4842-4470-8.
Xie, H., Yang, F., Hua, M., Liu, S., Hu, J., & He, Y. (2021). Grounding grid corrosion detection based on mini-batch gradient descent and greedy method. AIP Advances, 11(6), 1–11. doi:10.1063/5.0051678.
Si, Z., Wen, S., & Dong, B. (2019). NOMA Codebook Optimization by Batch Gradient Descent. IEEE Access, 7, 117274–117281. doi:10.1109/ACCESS.2019.2936483.
Chen, Y., & Shi, C. (2019). Network Revenue Management with Online Inverse Batch Gradient Descent Method. SSRN Electronic Journal. doi:10.2139/ssrn.3331939.
Hanifa, A., Fauzan, S. A., Hikal, M., & Ashfiya, M. B. (2021). Comparison of LSTM and GRU (RNN) Methods for Classification of Fake News in Indonesian. Engineering Dynamics, 17(1), 33. doi:10.20884/1.dr.2021.17.1.436. (In Indonesian).
Hsueh, B.-Y., Li, W., & Wu, I.-C. (2019). Stochastic Gradient Descent with Hyperbolic-Tangent Decay on Classification. 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). doi:10.1109/wacv.2019.00052.
Mignacco, F., Krzakala, F., Urbani, P., & Zdeborová, L. (2021). Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification. Journal of Statistical Mechanics: Theory and Experiment, 2021(12), 124008. doi:10.1088/1742-5468/ac3a80.
Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint. arXiv:1609.04747. doi:10.48550/arXiv.1609.04747
Khirirat, S., Feyzmahdavian, H. R., & Johansson, M. (2017). Mini-batch gradient descent: Faster convergence under data sparsity. 2017 IEEE 56th Annual Conference on Decision and Control (CDC). doi:10.1109/cdc.2017.8264077.
Gou, P., & Yu, J. (2018). A nonlinear ANN equalizer with mini-batch gradient descent in 40Gbaud PAM-8 IM/DD system. Optical Fiber Technology, 46, 113–117. doi:10.1016/j.yofte.2018.09.015.
Messaoud, S., Bradai, A., & Moulay, E. (2020). Online GMM Clustering and Mini-Batch Gradient Descent Based Optimization for Industrial IoT 4.0. IEEE Transactions on Industrial Informatics, 16(2), 1427–1435. doi:10.1109/TII.2019.2945012.
Hu, J., Gao, Y., Li, J., & Shang, X. (2019). Deep Learning Enables Accurate Prediction of Interplay Between lncRNA and Disease. Frontiers in Genetics, 10. doi:10.3389/fgene.2019.00937.
Murugan, P., & Durairaj, S. (2017). Regularization and optimization strategies in deep convolutional neural network. arXiv. preprint arXiv:1712.04711. doi:10.48550/arXiv.1712.04711.
Kukačka, J., Golkov, V., & Cremers, D. (2017). Regularization for deep learning: A taxonomy. arXiv:1710.10686. doi:10.48550/arXiv.1710.10686.
Gong, Y. (2016). Bernstein filter: A new solver for mean curvature regularized models. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China. doi:10.1109/icassp.2016.7471967.
Davvaz, B., Mukhlash, I., & Soleha, S. (2021). Himpunan Fuzzy DAN Rough Sets. Limits: Journal of Mathematics and Its Applications, 18(1), 79. doi:10.12962/limits.v18i1.7705.
Wang, Q., Qian, Y., Liang, X., Guo, Q., & Liang, J. (2018). Local neighborhood rough set. Knowledge-Based Systems, 153, 53–64. doi:10.1016/j.knosys.2018.04.023.
Zhan, J., Liu, Q., & Herawan, T. (2017). A novel soft rough set: Soft rough hemirings and corresponding multicriteria group decision making. Applied Soft Computing Journal, 54, 393–402. doi:10.1016/j.asoc.2016.09.012.
Jothi, G., Inbarani, H. H., Azar, A. T., & Devi, K. R. (2019). Rough set theory with Jaya optimization for acute lymphoblastic leukemia classification. Neural Computing and Applications, 31(9), 5175–5194. doi:10.1007/s00521-018-3359-7.
Wang, B., Li, J., & Sun, W. (2023). CD‐polytomous knowledge spaces and corresponding polytomous surmise systems. British Journal of Mathematical and Statistical Psychology, 76(1), 87-105. doi:10.1111/bmsp.12283.
Yao, Y., & Zhang, X. (2017). Class-specific attribute reducts in rough set theory. Information Sciences, 418–419, 601–618. doi:10.1016/j.ins.2017.08.038.
Zimmermann, H. J. (2011). Fuzzy set theory—and its applications. Springer Science & Business Media. doi:10.1007/978-94-010-0646-0.
Wiktorowicz, K., & Krzeszowski, T. (2020). Approximation of two-variable functions using high-order Takagi–Sugeno fuzzy systems, sparse regressions, and metaheuristic optimization. Soft Computing, 24(20), 15113–15127. doi:10.1007/s00500-020-05238-3.
Wu, D., Yuan, Y., Huang, J., & Tan, Y. (2020). Optimize TSK Fuzzy Systems for Regression Problems: Minibatch Gradient Descent with Regularization, DropRule, and AdaBound (MBGD-RDA). IEEE Transactions on Fuzzy Systems, 28(5), 1003–1015. doi:10.1109/TFUZZ.2019.2958559.
Rohmah, M. F., Ardiantoro, L., Putra, I. K. G. D., & Hartati, R. S. (2019). Forecasting the District Consumer Price Index in East Java with the Support Vector Regression Data Mining Method. Seminar Nasional Aplikasi Teknologi Informasi (SNATI), 3 August, 2019, Yogyakarta, Indonesia.
DOI: 10.28991/ESJ-2023-07-03-09
Refbacks
- There are currently no refbacks.
Copyright (c) 2023 Sugiyarto Surono, Zani Anjani Rafsanjani HSM, DESHINTA ARROVA DEWI, Annisa Eka Haryati, Tommy Tanu Wijaya