Abstract
For the cultural alienation of large-scale online open courses and the matching of key features, a new mapping method is proposed to effectively improve the learning ability of complex nonlinear data algorithms in TSK fuzzy systems. The tendency of cultural alienation is not only reflected in the curriculum knowledge of the text but also in the teaching process. This alienation emerges in the form of cultural obscuration, cultural locking, and cultural decoration. It makes the cultural implication in large-scale online courses gradually reduce, and even goes to the opposite side of culture. Thus, by extracting the convolution layer of the trained VGG19 network model as a feature of a large-scale open curriculum, the proposed features are individually weighted and then connected. The separate features are again convolved and aggregated, and the response function is used to determine connectivity between the open culture courses of the network. Aiming at the problem of high dimensionality of cultural features after single-layer TSK fuzzy feature mapping, the concept of multi-layer progressive fusion is proposed. A fuzzy feature mapping method based on multi-layer progressive fusion is deduced, which effectively solves the data confusion problem caused by excessive feature dimension after mapping. Finally, combined with the unsupervised learning algorithm, the retrieval of large-scale network disordered culture courses is realized. Research shows that the algorithm can effectively identify online culture courses of overlapping scenes without detailed matching process and geometric verification. Compared with the classical fuzzy clustering method, the algorithm has superior and stable performance.
Similar content being viewed by others
References
Oyserman D (2017) Culture three ways: culture and subcultures within countries. Annu Rev Psychol 68:435–463
Zou X, Jing L (2018) A mutual information-based two-phase memetic algorithm for large-scale fuzzy cognitive map learning. IEEE Trans Fuzzy Syst 26(4):2120–2134
De FJ, Davidoff J, Fagot J (2017) More accurate size contrast judgments in the Ebbinghaus illusion by a remote culture. J Exp Psychol Hum Percept Perform 33(3):738–742
Naderi E, Narimani H, Fathi M (2017) A novel fuzzy adaptive configuration of particle swarm optimization to solve large-scale optimal reactive power dispatch. Appl Soft Comput 53(C):441–456
Bendermacher GWG, Egbrink MGAO, Wolfhagen IHAP (2017) Unravelling quality culture in higher education: a realist review. High Educ 73(1):39–60
Schneider B, Gonzálezromá V, Ostroff C (2017) Organizational climate and culture: reflections on the history of the constructs in JAP. J Appl Psychol 102(3):468–482
Bhatia V, Rani R (2017) A parallel fuzzy clustering algorithm for large graphs using Pregel. Expert Syst Appl 78:135–144
Schneider B, Gonzálezromá V, Ostroff C (2017) Organizational climate and culture:reflections on the history of the constructs in Journal of Applied Psychology. J Appl Psychol 102(3):468–482
Han SK, Jin BP, Joo YH (2017) Decentralized sampled-data tracking control of large-scale fuzzy systems: an exact discretization approach. IEEE Access (99):1–1
Haenseler W, Sansom SN, Buchrieser JA (2017) Highly efficient human pluripotent stem cell microglia model displays a neuronal-co-culture-specific expression profile and inflammatory response. Stem Cell Rep 8(6):1727–1742
Beliakov G, Das G, Vu HQ (2018) Fuzzy connectives for efficient image reduction and speeding up image analysis. IEEE Access PP(99):1–1
Shaikh A, Anand S, Kapoor S (2017) Mouse bone marrow VSELs exhibit differentiation into three embryonic germ lineages and germ & hematopoietic cells in culture. Stem Cell Rev 13(2):202–216
Chen SL, Wu GS (2017) A cost and power efficient image compressor VLSI design with fuzzy decision and block partition for wireless sensor networks. IEEE Sensors J PP(99):1–1
Pamies D, Hartung T (2017) 21st century cell culture for 21st century toxicology. Chem Res Toxicol 30(1):43–52
Spaethling JM, Na YJ, Lee J (2017) Primary cell culture of live neurosurgically-resected aged adult human brain cells and single cell transcriptomics. Cell Rep 18(3):791–803
Jiang Y, Chung FL, Ishibuchi H (2015) Multitask TSK fuzzy system modeling by mining intertask common hidden structure [J]. IEEE Trans Cybern 45(3):548
Rodríguez-Fdez I, Mucientes M, Bugarín A (2016) FRULER: fuzzy rule learning through evolution for regression. Inf Sci 354:1–18
Deng Z, Choi KS, Cao L (2014) T2FELA: type-2 fuzzy extreme learning algorithm for fast training of interval type-2 TSK fuzzy logic system. IEEE Trans Neural Netw Learn Syst 25(4):664–676
Rodríguez-Fdez I, Mucientes M, Bugarín A (2016) S-FRULER: scalable fuzzy rule learning through evolution for regression. Knowl-Based Syst 110:255–266
Long NC, Meesad P, Unger H (2015) A highly accurate firefly based algorithm for heart disease prediction. Expert Syst Appl 42(21):8221–8231
Henkel J (2018) Joseph Henrich’s: the secret of our success—how culture is driving human evolution, domesticating our species, and making us smarter. J Bioecon 20(3):331–334
Fan J, Wang J, Min H (2014) Cooperative coevolution for large-scale optimization based on kernel fuzzy clustering and variable trust region methods. IEEE Trans Fuzzy Syst 22(4):829–839
Chanak P, Banerjee I (2016) Fuzzy rule-based faulty node classification and management scheme for large scale wireless sensor networks. Expert Syst Appl 45(C):307–321
Feng HM, Liao KL (2014) Hybrid evolutionary fuzzy learning scheme in the applications of traveling salesman problems. Inf Sci 270(270):204–225
Saleem N, Ahmad A, Zafar S (2014) A modified differential evolution algorithm for the solution of a large-scale unit commitment problem. Arab J Sci Eng 39(12):8889–8900
Kato K, Sakawa M, Ikegame T (2015) Improvement of genetic algorithms with decomposition procedures for large-scale multiobjective multidimensional 0-1 knapsack problems incorporating fuzzy goals. Electron Commun Jpn 83(12):62–69
Teh CY, Kai MT, Lim CP (2018) On the monotonicity property of the TSK fuzzy inference system: the necessity of the sufficient conditions and the monotonicity test. Int J Fuzzy Syst 20(6):1915–1924
Ren Q, Bigras P (2017) A highly accurate model-free motion control system with a Mamdani fuzzy feedback controller Combined with a TSK fuzzy feed-forward controller. J Intell Robot Syst 86(3):1–13
Guan JS, Lin CM, Ji GL (2017) Robust adaptive tracking control for manipulators based on a TSK fuzzy cerebellar model articulation controller. IEEE Access PP(99):1–1
Acknowledgments
Research Subject on the Teaching Reform of Vocational Education and Adult Education in 2018 in Jilin Province. “Reform and Practice of Big Data Application Courses in Financial Vocational Colleges,” 2018ZCY389.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yan, J., Sun, J. & Yang, D. Application of unsupervised TSK fuzzy algorithm in large-scale online culture courses. Pers Ubiquit Comput 24, 377–391 (2020). https://doi.org/10.1007/s00779-019-01266-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00779-019-01266-5