Abstract
Neutrosophic theory studies objects whose values vary in the sets of elements and are not true or false, but in between, that can be called by neutral, indeterminate, unclear, vague, ambiguous, incomplete or contradictory quantities. In this paper, we firstly introduce preliminaries on granular calculus and analysis related to single-valued neutrosophic functions. Based on horizontal membership functions approach, we establish some basic arithmetic operations of single-valued neutrosophic numbers, that red allow us to directly introduce the terms of neutrosophic function in usual mathematical formulas. Additionally, we build metrics on the space of single-valued neutrosophic numbers induced from Hamming distance. Then, we define some backgrounds on the limit, derivative and integral of single-valued neutrosophic functions. Finally, in order to demonstrate the usable of our theoretical results, we present some applications to well-known problems arising in engineering such as logistic model, the inverted pendulum system, Mass - Spring - Damper model.
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References
Ali M, Son L, Deli I, Tien ND (2017) Bipolar neutrosophic soft sets and applications in decision making. J Intell Fuzzy Syst 33(6):4077–4087
Ali M, Khan H, Son L, Smarandache F, Kandasamy W (2018) New Soft Set Based Class of Linear Algebraic Codes. Symmetry 10(10):510
Ali M, Son L, Thanh ND, Van Minh N (2018) A neutrosophic recommender system for medical diagnosis based on algebraic neutrosophic measures. Appl Soft Comput 71:1054–1071
Amal L, Son L, Chabchoub H (2018) SGA: spatial GIS-based genetic algorithm for route optimization of municipal solid waste collection. Environ Sci Pollut Res 25 (27):27569–27582
Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy sets Syst 20:87–96
Atanassov KT (2012) On intuitionistic fuzzy sets theory. Springer, Berlin
Atanassov KT (2017) Intuitionistic Fuzzy Logics. Springer, Cham
Bede B, Gal SG (2005) Generalizations of the differentiability of fuzzy number-valued functions with applications to fuzzy differential equations. Fuzzy Sets Syst 151:581–599
Bede B (2013) Mathematics of fuzzy sets and fuzzy logic. Springer, Berlin
Bede B, Stefanini L (2013) Generalized differentiability of fuzzy-valued functions. Fuzzy Sets Syst 230:119–141
Broumi S, Dey A, Bakali A, Talea M, Smarandache F, Son L, Koley D (2017) Uniform Single Valued Neutrosophic Graphs. Neutrosophic Sets Syst 17:42–49
Broumi S, Son L, Bakali A, Talea M, Smarandache F, Selvachandran G (2017) Computing Operational Matrices in Neutrosophic Environments: A Matlab Toolbox. Neutrosophic Sets & Systems:18:58–66
Çevik A, Topal S, Smarandache F (2018) Neutrosophic computability and enumeration. Symmetry 10(11):643–656
Çevik A, Topal S, Smarandache F (2018) Neutrosophic logic based quantum computing. Symmetry 10(11):656–667
Chakraborty A, Mondal SP, Ahmadian A, Senu N, Alam S, Salahshour S (2018) Different forms of triangular neutrosophic numbers, De-Neutrosophication techniques, and their applications. Symmetry 10(8):1–28
Chalapathi T, Kumar R (2018) Neutrosophic units of neutrosophic rings and fields. Neutrosophic Sets and Systems: 21:5–12
Chang SL, Zadeh LA (1972) On fuzzy mapping and control. IEEE Trans Syst Man Cybern 2:30–34
Dey A, Broumi S, Son L, Bakali A, Talea M, Smarandache F (2019) A new algorithm for finding minimum spanning trees with undirected neutrosophic graphs. Granular Computing 4(1):63–69. https://doi.org/10.1007/s41066-018-0084-7
Dey A, Son L, Kumar P, Selvachandran G, Quek S (2019) New Concepts on Vertex and Edge Coloring of Simple Vague Graphs. Symmetry 10(9):373
Doss S, Nayyar A, Suseendran G, Tanwar S, Khanna A, Thong PH (2018) APD-JFAD: Accurate Prevention and Detection of Jelly Fish Attack in MANET. IEEE Access 6:56954–56965
Dubois D, Prade H (1982) Towards fuzzy differential calculus. Part 3: differentiation. Fuzzy Sets Syst 8:225–233
Friedman M, Ming M, Kandel A (1996) Fuzzy derivatives and fuzzy Chauchy problems using LP metric. Fuzzy Log Found Ind Appl 8:57–72
Goetschel R, Voxman W (1986) Elementary fuzzy calculus. Fuzzy Sets Syst 18:31–43
Jha S, Kumar R, Son L, Chatterjee JM, Khari M, Yadav N, Smarandache F (2018) Neutrosophic soft set decision making for stock trending analysis. Evolving Systems. In Press. https://doi.org/10.1007/s12530-018-9247-7
Jiang W, Wei B (2018) Intuitionistic fuzzy evidential power aggregation operator and its application in multiple criteria decision-making. Int J Syst Sci 49(3):582–594
Joshi DK, Beg I, Kumar S (2018) Hesitant probabilistic fuzzy linguistic sets with applications in Multi-Criteria group decision making problems. Mathematics 6 (4):47
Khan M, Son L, Ali M, Chau H, Na N, Smarandache F (2018) Systematic review of decision making algorithms in extended neutrosophic sets. Symmetry 10(8):314
Majumdar P, Neutrosophic Sets and Its Applications to Decision Making. In: Acharjya D, Dehuri S, Sanyal S (eds) Computational Intelligence for Big Data Analysis. Adaptation, Learning, and Optimization, vol, 19. Springer, Cham
Mazandarani M, Pariz N (2018) Sub-optimal control of fuzzy linear dynamical systems under granular differentiability concept. ISA Trans 76:1–17
Mazandarani M, Pariz N, Kamyad AV (2018) Granular differentiability of Fuzzy-Number-Valued functions. IEEE Tran Fuzzy Syst 26(1):310–323
Mazandarani M, Zhao Y (2018) Fuzzy Bang-Bang control problem under granular differentiability. J. Frankl. Inst. 355(12):4931–4951
Nguyen GN, Son L, Ashour AS, Dey N (2019) A survey of the state-of-the-arts on neutrosophic sets in biomedical diagnoses. Int J Mach Learn Cybern 10(1):1–13
Peng JJ, Wang J, Wu XH, Wang J, Chen XH (2015) Multi-valued Neutrosophic Sets and Power Aggregation Operators with Their Applications in Multi-criteria Group Decision-making Problems. Int J Comput Intell Syst 8(2):345–363
Peng JJ, Wang J, Wang J, Zhang HY, Chen XH (2016) Simplified neutrosophic sets and their applications in multi-criteria group decision-making problems. Int J Syst Sci 47(10):2342–2358
Peng JJ, Wang J, Yang W (2017) A multi-valued neutrosophic qualitative flexible approach based on likelihood for multi-criteria decision-making problems. Int J Syst Sci 48(2):425–435
Piegat A, Landowski M (2016) Aggregation of inconsistent expert opinions with use of horizontal intuitionistic membership functions, Novel Developments in Uncertainty Representation and Processing. Springer, Berlin, pp 215–223
Piegat A, Landowski M (2017) Fuzzy arithmetic type-1 with HMFs, Uncertainty Modeling. Springer, Berlin, pp 233–250
Piegat A, Landowski M (2018) Solving different practical granular problems under the same system of equations. Granul Comput 3:39. https://doi.org/10.1007/s41066-017-0054-5
Puri ML, Ralescu DA (1983) Differentials of fuzzy functions. J Math Anal Appl 91:552–558
Sahin R, Liu P (2017) Possibility-induced simplified neutrosophic aggregation operators and their application to multi-criteria group decision-making. J Exper Theor Artif Intell 29(4):769–785
Sahin R, Zhang HY (2018) Induced simplified neutrosophic correlated aggregation operators for multi-criteria group decision-making. J Exper Theor Artif Intell 30 (2):279–292
Seikkala S (1987) On the fuzzy initial value problem. Fuzzy Sets Syst 24:319–330
Smarandache F (1998) Neutrosophy: Neutrosophic probability, set, and logic. American Research Press, Rehoboth
Smarandache F (2013) Introduction to neutrosophic measure, neutrosophic integral, and neutrosophic probability. Sitech & Education Publisher, Craiova
Smarandache F (2014) Introduction to neutrosophic statistics. Sitech & Education Publisher, Craiova
Smarandache F (2015) Neutrosophic precalculus and neutrosophic calculus. Europa-Nova, Brussels
Son L, Tuan TM (2016) A cooperative semi-supervised fuzzy clustering framework for dental X-ray image segmentation. Expert Syst Appl 46:380–393
Son L, Tuan TM (2017) Dental segmentation from X-ray images using semi-supervised fuzzy clustering with spatial constraints. Eng Appl Artif Intell 59:186–195
Son L, Chiclana F, Kumar R, Mittal M, Khari M, Chatterjee JM, Baik SW (2018) ARM–AMO: An efficient association rule mining algorithm based on animal migration optimization. Knowl-Based Syst 154:68–80
Son L, Fujita H (2019) Neural-fuzzy with representative sets for prediction of student performance. Appl Intell 49(2):172–187
Stefanini L, Bede B (2009) Generalized Hukuhara differentiability of interval-valued functions and interval differential equations. Nonlinear Anal: Theory Methods Appl 71:1311–1328
Taç F, Topal S, Smarandache F (2018) Clustering neutrosophic data sets and neutrosophic valued metric spaces. Symmetry 10(10):430–442
Thanh ND, Ali M (2017) A novel clustering algorithm in a neutrosophic recommender system for medical diagnosis. Cognitive Comput 9(4):526–544
Thanh ND, Son L, Ali M (2017) Neutrosophic recommender system for medical diagnosis based on algebraic similarity measure and clustering. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, pp 1–6
Thao NX, Cuong BC, Ali M, Lan LH (2018) Fuzzy Equivalence on Standard and Rough Neutrosophic Sets and Applications to Clustering Analysis. In: Information Systems Design and Intelligent Applications. Springer, Singapore, pp 834–842
Tian ZP, Zhang HY, Wang J, Wang J, Chen XH (2016) Multi-criteria decision-making method based on a cross-entropy with interval neutrosophic sets. Int J Syst Sci 47(15):3598–3608
Tuan TM, Ngan TT, Son L (2016) A novel semi-supervised fuzzy clustering method based on interactive fuzzy satisficing for dental X-ray image segmentation. Appl Intell 45(2):402–428
Tuan TM, Chuan PM, Ali M, Ngan TT, Mittal M, Son L (2018) Fuzzy and neutrosophic modeling for link prediction in social networks. Evolving Systems. In Press. https://doi.org/10.1007/s12530-018-9251-y
Tuong L, Son L, Vo M, Lee M, Baik S (2018) A Cluster-Based Boosting Algorithm for Bankruptcy Prediction in a Highly Imbalanced Dataset. Symmetry 10(7):250
Wang H, Smarandache F, Zhang Q, Sunderraman R (2010) Single valued neutrosophic sets, Multi-space and Multi-structure 4(2010):410–413
Wang CH, Wang J (2016) A multi-criteria decision-making method based on triangular intuitionistic fuzzy preference information. Intell Autom Soft Comput 22 (3):473–482
Ye J (2013) Multicriteria decision-making method using the correlation coefficient under single-valued neutrosophic environment. Int J Gen Syst 42:386–394
Ye J (2014) Clustering methods using Distance-Based similarity measures of Single-Valued neutrosophic sets. J Intell Syst 23:379–389
Ye J (2014) Improved correlation coefficients of single valued neutrosophic sets and interval neutrosophic sets for multiple attribute decision making. J Intell Fuzzy Syst 27:2453–2462
Ye J (2014) Single valued neutrosophic cross-entropy for multicriteria decision making problems. Appl Math Modell 38:1170–1175
Ye J (2017) Projection and bidirectional projection measures of single-valued neutrosophic sets and their decision-making method for mechanical design schemes. J Exper Theor Artif Intell 29(4):731–740
Ye J (2018) Multiple attribute group decision-making method with single-valued neutrosophic interval number information. International journal of systems science, In Press, pp 1–11
Zadeh LA (1965) Fuzzy Sets. Inf Control 8(3):338–353
Acknowledgements
The authors would like to thank the editor-in-chief, associate editor, and the anonymous referees for their helpful comments and valuable suggestions, which greatly improved this paper. This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 101.02-2018.311.
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Son, N.T.K., Dong, N.P., Son, L.H. et al. Towards granular calculus of single-valued neutrosophic functions under granular computing. Multimed Tools Appl 79, 16845–16881 (2020). https://doi.org/10.1007/s11042-019-7388-8
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DOI: https://doi.org/10.1007/s11042-019-7388-8