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B GIÁO DỤC VÀ ĐÀO TẠO VI N HÀN LÂM KHOA HOC VÀ CÔNG NGH VI T NAM HOC VI N KHOA HOC VÀ CÔNG NGH LƯƠNG TH± HONG LAN M T SO M R NG CÛA H SUY DIEN M PHỨC CHO BÀI TOÁN HO TR RA QUYET бNH LU N ÁN TIEN SĨ NGÀNH MÁY TÍNH Hà N i - 2021 LƯƠNG TH± HONG LAN M T SO M R NG CÛA H SUY DIEN M PHỨC CHO BÀI TOÁN HO TR RA QUYET бNH Chuyên ngành: Khoa hoc máy tính Mã so: 9.48.01.01 LU N ÁN TIEN SĨ NGÀNH MÁY TÍNH NGƯŐI HƯŐNG DAN KHOA HOC: PGS.TS Lê Hoàng Sơn PGS.TS Nguyen Long Giang Hà N i - 2021 L I CAM ĐOAN Tác giả xin cam đoan cơng trình nghiên cứu thân tác giả, đưoc hồn thành dưói sụ hưóng dȁn PGS.TS Lê Hoàng Sơn PGS.TS Nguyen Long Giang Các ket nghiên cứu ket lu n lu n án trung thục, không chép từ bat kỳ m t nguon dưói bat kỳ hình thức Vi c tham khảo nguon tài li u đưoc thục hi n trích dȁn ghi nguon tài li u tham khảo quy định Hà N i, ngày 19 tháng 06 năm 2021 Tác giả lu n án Lương Thị Hong Lan L I CÂM ƠN Lu n án đưoc hồn thành vói sụ nő lục khơng ngừng tác giả sụ giúp đõ het từ thay giáo hưóng dȁn, bạn bè ngưịi thân Đau tiên, tác giả xin bày tỏ lòng biet ơn chân thành sâu sac tói thay giáo hưóng dȁn PGS.TS Lê Hoàng Sơn PGS.TS Nguyen Long Giang Sụ t n tình bảo, hưóng dȁn đ ng viên thay dành cho tác giả suot thòi gian thục hi n lu n án không the ke het đưoc Tác giả xin gửi lòi cảm ơn tói thay, giáo cán b b ph n quản lý nghiên cứu sinh - Hoc vi n Khoa hoc Công ngh (Vi n Hàn lâm Khoa hoc Công ngh Vi t Nam), b ph n quản lý nghiên cứu sinh Vi n Công ngh thơng tin nhi t tình giúp đõ tạo mơi trưịng nghiên cứu tot đe tác giả hồn thành cơng trình Tác giả xin chân thành cảm ơn anh chị em Lab Tại Vi n Công ngh thông tin - Đại hoc Quoc gia Hà N i giúp đõ tác giả suot trình hoc t p nghiên cứu Lab Tác giả xin chân thành cảm ơn tói Ban Giám hi u trưòng Đại hoc Sư phạm, Đại hoc Thái Nguyên, đong nghi p khoa Toán, nơi tác giả công tác năm đau nghiên cứu sinh; Ban Giám hi u trưòng Đại hoc Thủy Loi Hà N i, đong nghi p khoa Công ngh thông tin, nơi tác giả hi n công tác đeu đ ng viên, giúp đõ tác giả cơng tác đe tác giả có thịi gian t p trung nghiên cứu hoàn thành lu n án thòi hạn Đ c bi t tác giả xin bày tỏ lịng biet ơn sâu sac tói Bo, Me, em gia đình, ngưịi ln dành cho tình cảm nong am sẻ chia lúc khó khăn cu c song, ln đ ng viên giúp đõ tơi q trình nghiên cứu Cảm ơn gái ln ngoan ngỗn ủng h đe me t p trung nghiên cứu, hoàn thành lu n án Lu n án quà tinh than mà trân gửi t ng đen thành viên Gia đình Tơi xin trân cảm ơn! Hà N i, ngày 19 tháng 06 năm 2021 Ngưòi thục hi n Lương Thị Hong Lan v MỤC LỤC Danh mục bang vi Danh mục hình vẽ, đo thị vii M ĐAU Chương 1 TONG QUAN NGHIÊN CỨU VÀ CƠ S LÝ THUYET 1.1 Giói thi u 1.2 Van đe H suy dien mò H hő tro quyet định 1.3 Tong quan nghiên cứu liên quan 10 1.3.1 H suy dien mờ 11 1.3.2 Các h phát trien dựa t p mờ phŕc 14 1.3.3 Các van đe ton can giái quyet h CFIS hi n 19 1.4 Cơ sỏ lý thuyet 20 1.4.1 T p mờ 21 1.4.2 T p mờ phŕc 21 1.4.3 Các phép toán t p mờ phŕc 24 1.4.4 Logic mờ phŕc 27 1.4.5 Đ đo mờ đ đo mờ phŕc 28 1.5 Dữ li u thục nghi m 30 1.5.1 B dr li u chuȁn 30 1.5.2 B dr li u thực- B nh gan Liver 31 1.5.3 Các đ đo đánh giá thực nghi m 32 1.6 Ket Chương 33 Chương XÂY DỰNG H SUY DIEN M PHỨC DẠNG MAMDANI (M-CFIS) 34 2.1 Giói thi u .34 2.2 Đe xuat toán tử t-chuan t- đoi chuan mò phức .36 2.2.1 Toán tr t-chuȁn t-đoi chuȁn 37 2.2.2 Toán tr t-chuȁn t-đoi chuȁn mờ phŕc .38 2.2.3 Ví dn minh hoa hő trợ quyet định 41 2.3 H suy dien mò phức Mamdani (M-CFIS) .44 2.3.1 Đe xuat h suy dien mờ phŕc Mamdani 44 2.3.2 Các lựa chon sr dnng h suy dien mờ phŕc Mamdani 45 2.3.3 Cau trúc h suy dien mò phức Mamdani 47 2.3.4 Ví dn so minh hoa mơ hình suy dien M-CFIS 49 2.3.5 Thr nghi m đánh giá ket 51 2.4 Ket Chương 53 Chương TINH GIÂM H LU T TRONG H SUY DIEN M PHỨC MAMDANI (M-CFIS-R) 55 3.1 Giói thi u .55 3.2 Đe xuat đ đo tương tụ mò phức 60 3.2.1 Đ đo tương tự mờ phŕc Cosine 61 3.2.2 Đ đo tương tự mờ phŕc Dice .62 3.2.3 Đ đo tương tự mờ phŕc Jaccard 63 3.3 Đe xuat mơ hình h suy dien M-CFIS-R 64 3.3.1 Ý tướng xây dựng mơ hình 64 3.3.2 Phan Training 65 3.3.3 Phan Testing 70 3.4 Thử nghi m đánh giá ket .71 3.4.1 Ket thực nghi m b dr li u UCI .71 3.4.2 Ket thực nghi m b dr li u thực 73 3.5 Ket Chương 75 Chương M R NG H SUY DIEN M PHỨC MAMDANI V I ĐO TH± TRI THỨC (M-CFIS-FKG) 77 4.1 Giói thi u 77 4.2 M t so mỏ r ng mơ hình M-CFIS-R .79 4.2.1 H suy dien mờ phŕc Sugeno Tsukamoto 79 4.2.2 Đ đo mờ phŕc dựa lý thuyet t p hợp 80 4.2.3 Tích phân mờ phŕc .86 4.3 Đe xuat mơ hình h suy dien mò phức M-CFIS-FKG .93 4.3.1 Ý tướng xây dựng mơ hình 93 4.3.2 Xây dựng đo thị tri thŕc mờ 95 4.3.3 Thu t toán suy dien nhanh đo thị tri thŕc mờ 96 4.3.4 Ví dn minh hoa h suy dien mờ phŕc M-CFIS-FKG .98 4.4 Thục nghi m đánh giá ket 103 4.4.1 Thực nghi m .103 4.4.2 Ket thực nghi m 104 4.5 Ket Chương 112 KET LU N VÀ HƯ NG PHÁT TRIEN 114 Những ket lu n án 114 Hưóng phát trien lu n án 116 TÀI LI U THAM KHÂO 119 Kí hi u viet tat STT Từ tat FS CFS CFL FIS 10 11 12 13 14 15 16 17 Tieng anh Fuzzy Set Complex Fuzzy Set Complex Fuzzy Logic Fuzzy Inference System Complex Fuzzy CFIS Inference System Intituition Fuzzy IFIS Inference System Adaptive ANFIS Neuro Fuzzy Inference System Complex CANFIS Neuro-Fuzzy Inference System Adaptive ANCFIS Neuro Complex Fuzzy Inference System CNS Neutrosophic phức MCDM Multicriteria decision making Fast FISA Inference Search Algorithm KG FKG mò Mamdani Fuzzy M-FIS Inference System Mamdani M-CFIS Complex Fuzzy Inference System Mamdani Complex Fuzzy M-CFIS-R Inference System Reduce Rule Dien dải T p mò T p mò phức Logic mò phức H suy dien H suy dien mò phức H suy dien mị trục cảm H suy dien mị noron thích nghi H suy dien mị noron thích nghi phức Mạng noron giá trị mị phức thích nghi Complex Neutrosophic Set T p H hő tro quyet định đa tiêu chí Thu t tốn tìm kiem suy dien nhanh Knowledge Graph Đo thị tri thức Fuzzy Knowledge Graph Đo thị tri thức H suy dien mò Mamdani H suy dien Mamdani mò phức H suy dien mò Mamdani - giảm lu t phức 18 MCFI SFK G 19 20 21 22 RA NCF IS FAN CFI S suy dien mò phức Mamdani Complex Fuzzy H Inference System Fuzzy- Mamdani - Đo thị tri thức mò Knowledge Graph GRC UCI UCI Randomized AdaptiveNetwork Based Fuzzy Inference System Fast Adaptive-Network Based Fuzzy Inference System Granular Computing Tính tốn hạt UC Irvine Machine Kho li u chuan Mạng nơ ron giá trị mị phức thích nghi ngȁu nhiên Mạng nơ ron giá trị mị phức thích nghi nhanh Danh mục bang 1.1 Các b li u thục nghi m chuan Benchmark 31 1.2 Các thu c tính li u đau vào t p li u b nh gan Liver 32 2.1 Ma tr n quyet định dụa mȁu li u 42 2.2 Ma tr n quyet định mò 43 2.3 Ma tr n chuan hóa .43 2.4 Ma tr n quyet định mò 43 2.5 Ma tr n quyet định ket .44 2.6 B li u đau vào 50 2.7 B sỏ lu t .50 4.1 H sỏ lu t mò phức 95 4.2 Kịch 103 4.3 Kịch 103 [10] A Bakhshipour, H Zareiforoush, and I Bagheri, “Application of decision trees and fuzzy inference system for quality classification and modeling of black and green tea based on visual features,” Journal of Food Measurement and Characterization, pp 1–15, 2020 [11] E Pourjavad and A Shahin, “The application of mamdani fuzzy inference system in evaluating green supply chain management performance,” International Journal of Fuzzy Systems, vol 20, no 3, pp 901–912, 2018 [12] N Priyadarshi, F Azam, A K Sharma, and M Vardia, “An adaptive neuro- fuzzy inference system-based intelligent grid-connected photovoltaic power generation,” in Advances in Computational Intelligence, pp 3–14, Springer, 2020 [13] A C Adoko and S Yagiz, “Fuzzy inference system-based for tbm field penetration index estimation in rock mass,” Geotechnical and Geological Engineering, vol 37, no 3, pp 1533–1553, 2019 [14] J.-S Jang, “Anfis: adaptive-network-based fuzzy inference system,” IEEE transactions on systems, man, and cybernetics, vol 23, no 3, pp 665– 685, 1993 [15] D Karaboga and E Kaya, “Adaptive network based fuzzy inference system (anfis) training approaches: a comprehensive survey,” Artificial Intelligence Review, vol 52, no 4, pp 2263–2293, 2019 [16] S Subbulakshmi, G Marimuthu, and N Neelavathy, “Application of sanfis method in coronary artery disease,” Malaya Journal of Matematik (MJM), no 1, 2019, pp 535–538, 2019 [17] R Razavi, A Sabaghmoghadam, A Bemani, A Baghban, K.-w Chau, and E Salwana, “Application of anfis and lssvm strategies for estimating thermal conductivity enhancement of metal and metal oxide based nanofluids,” Engineering Applications of Computational Fluid Mechanics, vol 13, no 1, pp 560–578, 2019 [18] G K Tairidis, N Stojanovic, D Stamenkovic, and G E Stavroulakis, “Neuro- fuzzy techniques and natural risk management applications of anfis models in floods and comparison with other models,” in Natural Risk Management and Engineering, pp 169–189, Springer, 2020 [19] P Sihag, N Tiwari, and S Ranjan, “Prediction of unsaturated hydraulic conductivity using adaptive neuro-fuzzy inference system (anfis),” ISH Journal of Hydraulic Engineering, vol 25, no 2, pp 132–142, 2019 [20] A Azad, M Manoochehri, H Kashi, S Farzin, H Karami, V Nourani, and J Shiri, “Comparative evaluation of intelligent algorithms to improve adaptive neuro-fuzzy inference system performance in precipitation modelling,” Journal of Hydrology, vol 571, pp 214–224, 2019 [21] Y K Semero, J Zhang, and D Zheng, “Emd–pso–anfis-based hybrid approach for short-term load forecasting in microgrids,” IET Generation, Transmission & Distribution, vol 14, no 3, pp 470–475, 2019 [22] P Hájek and V Olej, “Adaptive intuitionistic fuzzy inference systems of takagi- sugeno type for regression problems,” in IFIP International Conference on Artificial Intelligence Applications and Innovations, pp 206– 216, Springer, 2012 [23] P Hajek and V Olej, “Defuzzification methods in intuitionistic fuzzy inference systems of takagi-sugeno type: The case of corporate bankruptcy prediction,” in 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp 232–236, IEEE, 2014 [24] A Hernandez-Aguila, M Garcia-Valdez, and O Castillo, “A proposal for an intuitionistic fuzzy inference system,” in 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp 1294–1300, IEEE, 2016 [25] E Egrioglu, E Bas, O C Yolcu, and U Yolcu, “Intuitionistic time series fuzzy inference system,” Engineering Applications of Artificial Intelligence, vol 82, pp 175–183, 2019 [26] C Luo, C Tan, X Wang, and Y Zheng, “An evolving recurrent interval type-2 intuitionistic fuzzy neural network for online learning and time series prediction,” Applied Soft Computing, vol 78, pp 150–163, 2019 [27] E Bas, U Yolcu, and E Egrioglu, “Intuitionistic fuzzy time series functions approach for time series forecasting,” Granular Computing, pp 1– 11, 2020 [28] P H Thong et al., “Some novel hybrid forecast methods based on picture fuzzy clustering for weather nowcasting from satellite image sequences,” Applied Intelligence, vol 46, no 1, pp 1–15, 2017 [29] P Van Viet, P Van Hai, et al., “Picture inference system: a new fuzzy inference system on picture fuzzy set,” Applied Intelligence, vol 46, no 3, pp 652–669, 2017 [30] L H Son, “Measuring analogousness in picture fuzzy sets: from picture distance measures to picture association measures,” Fuzzy Optimization and Decision Making, vol 16, pp 359–378, 2017 [31] L H Son, “Generalized picture distance measure and applications to picture fuzzy clustering,” Applied Soft Computing, vol 46, no C, pp 284– 295, 2016 [32] P H Thong et al., “A novel automatic picture fuzzy clustering method based on particle swarm optimization and picture composite cardinality,” Knowledge- Based Systems, vol 109, pp 48–60, 2016 [33] P H Thong et al., “Picture fuzzy clustering for complex data,” Engineering Applications of Artificial Intelligence, vol 56, pp 121–130, 2016 [34] L H Son, “A novel kernel fuzzy clustering algorithm for geodemographic analysis,” Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, vol 317, no C, pp 202–223, 2015 [35] P Van Viet, H T M Chau, P Van Hai, et al., “Some extensions of membership graphs for picture inference systems,” in 2015 seventh international conference on knowledge and systems engineering (KSE), pp 192–197, IEEE, 2015 [36] D Ramot, R Milo, M Friedman, and A Kandel, “Complex fuzzy sets,” IEEE Transactions on Fuzzy Systems, vol 10, no 2, pp 171–186, 2002 [37] H Garg and D Rani, “Some results on information measures for complex intuitionistic fuzzy sets,” International Journal of Intelligent Systems, vol 34, no 10, pp 2319–2363, 2019 [38] J.-P Fan, R Cheng, and M.-Q Wu, “Extended edas methods for multi- criteria group decision-making based on iv-cfswaa and iv-cfswga operators with interval-valued complex fuzzy soft information,” IEEE Access, vol 7, pp 105546–105561, 2019 [39] S Faizi, Multi-Criteria Decision Making Techniques Based on Some Extensions of Fuzzy Set PhD thesis, University of Management and Technology, Lahore, 2019 [40] H Garg and D Rani, “Robust averaging–geometric aggregation operators for complex intuitionistic fuzzy sets and their applications to mcdm process,” Arabian Journal for Science and Engineering, vol 45, no 3, pp 2017–2033, 2020 [41] K Ullah, T Mahmood, Z Ali, and N Jan, “On some distance measures of complex pythagorean fuzzy sets and their applications in pattern recognition,” Complex & Intelligent Systems, vol 6, no 1, pp 15–27, 2020 [42] Y Al-Qudah and N Hassan, “Complex multi-fuzzy soft expert set and its application,” Int J Math Comput Sci, vol 14, pp 149–176, 2019 [43] P K Singh, “Bipolar δ-equal complex fuzzy concept lattice with its application,” Neural Computing and Applications, pp 1–18, 2019 [44] D Ramot, M Friedman, G Langholz, and A Kandel, “Complex fuzzy logic,” IEEE Transactions on Fuzzy Systems, vol 11, no 4, pp 450–461, 2003 [45] J Man, Z Chen, and S Dick, “Towards inductive learning of complex fuzzy inference systems,” in NAFIPS 2007-2007 Annual Meeting of the North American Fuzzy Information Processing Society, pp 415–420, IEEE, 2007 [46] Z Chen, S Aghakhani, J Man, and S Dick, “Ancfis: A neurofuzzy architecture employing complex fuzzy sets,” IEEE Transactions on Fuzzy Systems, vol 19, no 2, pp 305–322, 2010 [47] Y Liu and F Liu, “An adaptive neuro-complex-fuzzy-inferential modeling mechanism for generating higher-order tsk models,” Neurocomputing, vol 365, pp 94–101, 2019 [48] O Yazdanbakhsh and S Dick, “Fancfis: Fast adaptive neuro-complex fuzzy inference system,” International Journal of Approximate Reasoning, vol 105, pp 417–430, 2019 [49] E H Mamdani, “Application of fuzzy algorithms for control of simple dynamic plant,” in Proceedings of the institution of electrical engineers, vol 121, pp 1585–1588, IET, 1974 [50] T Takagi and M Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE transactions on systems, man, and cybernetics, no 1, pp 116–132, 1985 [51] Y Li and Y.-T Jang, “Complex adaptive fuzzy inference systems,” in Soft Computing in Intelligent Systems and Information Processing Proceedings of the 1996 Asian Fuzzy Systems Symposium, pp 551–556, IEEE, 1996 [52] A Deshmukh, A Bavaskar, P Bajaj, and A Keskar, “Implementation of complex fuzzy logic modules with vlsi approach,” International Journal on Computer Science and Network Security, vol 8, pp 172–178, 2008 [53] O Yazdanbakhsh and S Dick, “Forecasting of multivariate time series via complex fuzzy logic,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol 47, no 8, pp 2160–2171, 2017 [54] M Yeganejou and S Dick, “Inductive learning of classifiers via complex fuzzy sets and logic,” in 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp 1–6, IEEE, 2017 [55] O Yazdanbakhsh and S Dick, “A systematic review of complex fuzzy sets and logic,” Fuzzy Sets and Systems, vol 338, pp 1–22, 2018 [56] C Li and T.-W Chiang, “Complex neurofuzzy arima forecasting—a new approach using complex fuzzy sets,” IEEE Transactions on Fuzzy Systems, vol 21, no 3, pp 567–584, 2012 [57] H Bustince, E Barrenechea, M Pagola, J Fernandez, Z Xu, B Bedregal, J Montero, H Hagras, F Herrera, and B De Baets, “A historical account of types of fuzzy sets and their relationships,” IEEE Transactions on Fuzzy Systems, vol 24, no 1, pp 179–194, 2015 [58] J Buckley and Y Qu, “Fuzzy complex analysis i: differentiation,” Fuzzy Sets and Systems, vol 41, no 3, pp 269–284, 1991 [59] J J Buckley, “Fuzzy complex analysis ii: integration,” Fuzzy Sets and Systems, vol 49, no 2, pp 171–179, 1992 [60] Z Guang-Quan, “Fuzzy limit theory of fuzzy complex numbers,” Fuzzy Sets and Systems, vol 46, no 2, pp 227–235, 1992 [61] Z Guangquan, “Fuzzy distance and limit of fuzzy numbers [j],” Fuzzy Systems and Mathematics, vol 1, 1992 [62] A Azam, B Fisher, and M Khan, “Common fixed point theorems in complex valued metric spaces,” Numerical Functional Analysis and Optimization, vol 32, no 3, pp 243–253, 2011 [63] G Zhang, T S Dillon, K.-Y Cai, J Ma, and J Lu, “Operation properties and δ-equalities of complex fuzzy sets,” International journal of approximate reasoning, vol 50, no 8, pp 1227–1249, 2009 [64] A U M Alkouri and A R Salleh, “Linguistic variable, hedges and several distances on complex fuzzy sets,” Journal of Intelligent & Fuzzy Systems, vol 26, no 5, pp 2527–2535, 2014 [65] M Guido, A Mangia, G Faa, et al., “Chronic viral hepatitis: the histology report,” Digestive and Liver Disease, vol 43, pp S331–S343, 2011 [66] F Camastra, A Ciaramella, V Giovannelli, M Lener, V Rastelli, A Staiano, G Staiano, and A Starace, “A fuzzy decision system for genetically modified plant environmental risk assessment using mamdani inference,” Expert Systems with Applications, vol 42, no 3, pp 1710–1716, 2015 [67] B Gayathri and C Sumathi, “Mamdani fuzzy inference system for breast cancer risk detection,” in 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp 1–6, IEEE, 2015 [68] S Thakur, S Raw, R Sharma, and P Mishra, “Detection of type of thalassemia disease in patients: A fuzzy logic approach,” International Journal of Applied Pharmaceutical Sciences and Research, vol 1, no 02, pp 88–95, 2016 [69] P Mamoria and D Raj, “Comparison of mamdani fuzzy inference system for multiple membership functions,” International Journal of Image, Graphics and Signal Processing, vol 8, no 9, p 26, 2016 [70] M D RUZˇ IC´ , J Skenderovic´, and K T LESIC´ , “Application of the mamdani fuzzy inference system to measuring hrm performance in hotel companies–a pilot study,” 2016 [71] P K Borkar, M Jha, M Qureshi, and G Agrawal, “Performance assessment of heat exchanger using mamdani based adaptive neuro-fuzzy inference system (m-anfis) and dynamic fuzzy reliability modeling 2014,” International Journal of Innovative Research in Science, Engineering and Technology (An ISO 3297: 2007 Certified Organization), vol 3, no 9, 2014 [72] Y Chai, L Jia, and Z Zhang, “Mamdani model based adaptive neural fuzzy inference system and its application,” International Journal of Computational Intelligence, vol 5, no 1, pp 22–29, 2009 [73] E P Klement and R Mesiar, Logical, algebraic, analytic and probabilistic aspects of triangular norms Elsevier, 2005 [74] H T Nguyen, C L Walker, and E A Walker, A first course in fuzzy logic CRC press, 2018 [75] R R Yager and D P Filev, “Unified structure and parameter identification of fuzzy models,” IEEE Transactions on Systems, Man, and Cybernetics, vol 23, no 4, pp 1198–1205, 1993 [76] H Ishibuchi, K Nozaki, N Yamamoto, and H Tanaka, “Selecting fuzzy ifthen rules for classification problems using genetic algorithms,” IEEE Transactions on fuzzy systems, vol 3, no 3, pp 260–270, 1995 [77] J Yen and L Wang, “Simplifying fuzzy rule-based models using orthogonal transformation methods,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol 29, no 1, pp 13–24, 1999 [78] L Wang and R Langari, “Building sugeno-type models using fuzzy discretization and orthogonal parameter estimation techniques,” IEEE Transactions on Fuzzy Systems, vol 3, no 4, pp 454–458, 1995 [79] G E Tsekouras, “Fuzzy rule base simplification using multidimensional scaling and constrained optimization,” Fuzzy sets and systems, vol 297, pp 46– 72, 2016 [80] W Pedrycz, “From fuzzy rule-based systems to granular fuzzy rule-based systems: a study in granular computing,” in Combining Experimentation and Theory, pp 151–162, Springer, 2012 [81] H Bellaaj, R Ketata, and M Chtourou, “A new method for fuzzy rule base reduction,” Journal of Intelligent & Fuzzy Systems, vol 25, no 3, pp 605–613, 2013 [82] J L S L P Heim, S Hellmann and T Stegemann, “Multitask tsk fuzzy system modeling by jointly reducing rules and consequent parameters,” Transactions on Systems, vol 25, no 3, pp 605–613, 2019 [83] G Wang and X Li, “Generalized lebesgue integrals of fuzzy complex valued functions,” Fuzzy Sets and Systems, vol 127, no 3, pp 363–370, 2002 [84] L.-C Jang and H.-M Kim, “On choquet integrals with respect to a fuzzy complex valued fuzzy measure of fuzzy complex valued functions,” International Journal of Fuzzy Logic and Intelligent Systems, vol 10, no 3, pp 224–229, 2010 [85] L.-C Jang and H.-M Kim, “Some properties of choquet integrals with respect to a fuzzy complex valued fuzzy measure.,” Int J Fuzzy Logic and Intelligent Systems, vol 11, no 2, pp 113–117, 2011 [86] S.-q Ma, D.-j Peng, and D.-y Li, “Fuzzy complex value measure and fuzzy complex value measurable function,” in Fuzzy Information and Engineering, pp 187–192, Springer, 2009 [87] S.-q Ma, F.-c Chen, and Z.-q Zhao, “Choquet type fuzzy complexvalued integral and its application in classification,” in Fuzzy Engineering and Operations Research, pp 229–237, Springer, 2012 [88] S.-q Ma, M.-q Chen, and Z.-q Zhao, “The complex fuzzy measure,” in Fuzzy Information & Engineering and Operations Research & Management, pp 137– 145, Springer, 2014 [89] S Ma and S Li, “Complex fuzzy set-valued complex fuzzy measures and their properties,” The Scientific World Journal, vol 2014, 2014 [90] S.-q Ma and S.-g Li, “Complex fuzzy set-valued complex fuzzy integral and its convergence theorem,” in Fuzzy Systems & Operations Research and Management, pp 143–155, Springer, 2016 [91] S Ma, D Peng, and Z Zhao, “Generalized complex fuzzy set-valued integrals and their properties,” in 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp 906–910, IEEE, 2016 [92] R T Ngan, M Ali, D E Tamir, N D Rishe, A Kandel, et al., “Representing complex intuitionistic fuzzy set by quaternion numbers and applications to decision making,” Applied Soft Computing, vol 87, p 105961, 2020 [93] S Dai, L Bi, and B Hu, “Distance measures between the interval-valued complex fuzzy sets,” Mathematics, vol 7, no 6, p 549, 2019 [94] K Mondal, S Pramanik, and B C Giri, “Some similarity measures for madm under a complex neutrosophic set environment,” in Optimization Theory Based on Neutrosophic and Plithogenic Sets, pp 87–116, Elsevier, 2020 [95] A Z LOTFI, “The key roles of information granulation and fuzzy logic in human reasoning, concept formulation and computing with words,” in FUZZ- IEEE’96—Fifth IEEE International Conference on Fuzzy Systems, pp 8–11 [96] T Lin, “Granular computing, announcement of the bisc special interest group on granular computing [z] 1997.” [97] Y Yao and N Zhong, “Granular computing using information tables,” in Data mining, rough sets and granular computing, pp 102–124, Springer, 2002 [98] J T Yao, A V Vasilakos, and W Pedrycz, “Granular computing: perspectives and challenges,” IEEE Transactions on Cybernetics, vol 43, no 6, pp 1977– 1989, 2013 [99] J T Yao, A V Vasilakos, and W Pedrycz, “Granular computing: perspectives and challenges,” IEEE Transactions on Cybernetics, vol 43, no 6, pp 1977– 1989, 2013 [100] P Artiemjew, “Natural versus granular computing: Classifiers from granular structures,” in International Conference on Rough Sets and Current Trends in Computing, pp 150–159, Springer, 2008 [101] S Butenkov, A Zhukov, A Nagorov, and N Krivsha, “Granular computing models and methods based on the spatial granulation,” Procedia Computer Science, vol 103, no C, pp 295–302, 2017 [102] F M Bianchi, S Scardapane, A Rizzi, A Uncini, and A Sadeghian, “Granular computing techniques for classification and semantic characterization of structured data,” Cognitive Computation, vol 8, no 3, pp 442–461, 2016 [103] N Krivsha, V Krivsha, Z Beslaneev, and S Butenkov, “Greedy algorithms for granular computing problems in spatial granulation technique,” Procedia Computer Science, vol 103, pp 303–307, 2017 [104] W Zhang, G Wang, W Liu, and J Fang, “An introduction to fuzzy mathematics,” Xi’an Jiaotong University Press, Xi’an, 1991 [105] H Q Truong, L T Ngo, and W Pedrycz, “Granular fuzzy possibilistic c- means clustering approach to dna microarray problem,” Knowledge-Based Systems, vol 133, pp 53–65, 2017 [106] P Heim, S Hellmann, J Lehmann, S Lohmann, and T Stegemann, “Relfinder: Revealing relationships in rdf knowledge bases,” in International Conference on Semantic and Digital Media Technologies, pp 182–187, Springer, 2009 [107] T Yu, J Li, Q Yu, Y Tian, X Shun, L Xu, L Zhu, and H Gao, “Knowledge graph for tcm health preservation: design, construction, and applications,” Artificial Intelligence in Medicine, vol 77, pp 48–52, 2017 [108] L Shao, Y Duan, X Sun, Q Zou, R Jing, and J Lin, “Bidirectional value driven design between economical planning and technical implementation based on data graph, information graph and knowledge graph,” in 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA), pp 339–344, IEEE, 2017 [109] Z Wang, J Zhang, J Feng, and Z Chen, “Knowledge graph embedding by translating on hyperplanes.,” in Aaai, vol 14, pp 1112–1119, Citeseer, 2014 [110] J Qiu, Q Du, K Yin, S.-L Zhang, and C Qian, “A causality mining and knowledge graph based method of root cause diagnosis for performance anomaly in cloud applications,” Applied Sciences, vol 10, no 6, p 2166, 2020 [111] L He and P Jiang, “Manufacturing knowledge graph: a connectivism to answer production problems query with knowledge reuse,” IEEE Access, vol 7, pp 101231–101244, 2019 [112] R Lijuan, L Jun, and G Wei, “Multi-source knowledge embedding research of knowledge graph,” in 2019 IEEE 3rd International Conference on Circuits, Systems and Devices (ICCSD), pp 163–166, IEEE, 2019 [113] J Long, Z Chen, W He, T Wu, and J Ren, “An integrated framework of deep learning and knowledge graph for prediction of stock price trend: An application in chinese stock exchange market,” Applied Soft Computing, p 106205, 2020 [114] X Chen, S Jia, and Y Xiang, “A review: Knowledge reasoning over knowledge graph,” Expert Systems with Applications, vol 141, p 112948, 2020 [115] S Yoo and O Jeong, “Automating the expansion of a knowledge graph,” Expert Systems with Applications, vol 141, p 112965, 2020 [116] H Liu, Y Li, R Hong, Z Li, M Li, W Pan, A Glowacz, and H He, “Knowledge graph analysis and visualization of research trends on driver behavior,” Journal of Intelligent & Fuzzy Systems, vol 38, no 1, pp 495– 511, 2020 [117] H Wang, C Shah, P Sathaye, A Nahata, and S Katariya, “Service application knowledge graph and dependency system,” in 2019 34th IEEE/ACM International Conference on Automated Software Engineering Workshop (ASEW), pp 134–136, IEEE, 2019 [118] E Petrova, P Pauwels, K Svidt, and R L Jensen, “Towards data-driven sustainable design: decision support based on knowledge discovery in disparate building data,” Architectural Engineering and Design Management, vol 15, no 5, pp 334–356, 2019 [119] L Shi, S Li, X Yang, J Qi, G Pan, and B Zhou, “Semantic health knowledge graph: Semantic integration of heterogeneous medical knowledge and services,” BioMed research international, vol 2017, 2017 [120] X Tao, T Pham, J Zhang, J Yong, W P Goh, W Zhang, and Y Cai, “Mining health knowledge graph for health risk prediction,” World Wide Web, pp 1–22, 2020 ... 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