Cognitive Computation

, Volume 11, Issue 5, pp 613–629 | Cite as

Bidirectional Cognitive Computing Model for Uncertain Concepts

  • Changlin XuEmail author
  • Guoyin Wang


Most intelligent computing models are inspired by various human/natural/social intelligence mechanisms during the past 60 years. Achievements of cognitive science could give much inspiration to artificial intelligence. Cognitive computing is one of the core fields of artificial intelligence. It aims to develop a coherent, unified, universal mechanism inspired by human mind’s capabilities. It is one of the most critical tasks for artificial intelligence researchers to develop advanced cognitive computing models. The human cognition has been researched in many fields. Some uncertain theories are briefly analyzed from the perspective of cognition based on concepts. In classical intelligent information systems, original data are collected from environment at first; usually, useful information is extracted through analyzing the input data then, it is used to solve some problem at last. There is a common characteristic between traditional machine learning, data mining, and knowledge discovery models. That is, knowledge is always transformation from data. From the point of view of granular computing, it is a unidirectional transformation from finer granularity to coarser granularity. Inspired by human’s granular thinking and the cognition law of “global precedence”, the human cognition process is from coarser granularity to finer granularity. Generally speaking, concepts (information and knowledge) in a higher granularity layer would be more uncertain than the ones in a lower granularity layer. A concept in a higher granularity layer would be the abstraction of some objects (data or concepts in a lower granularity layer). Obviously, there is a contradiction between the unidirectional transformation mechanism “from finer granularity to coarser granularity” of traditional intelligent information systems with the global precedence law of human cognition. That is, the human cognition are different the computer cognition for uncertain concept. The human cognition for knowledge (or concept) is based on the intension of concept, while the computing of computer (or machine) is based on the extension. In order to integrate the human cognition of “from coarser to finer” and the computer’s information processing of “from finer to coarser”, a new cognitive computing model, bidirectional cognitive computing model between the intension and extension of uncertain concepts, is proposed. The purpose of the paper is to establish the relationship between the human brain computing mode (computing based on intension of concept) and the machine computing mode (computing based on extension of concept) through the way of computation. The cloud model theory as a new cognition model for uncertainty proposed by Li in 1995 based on probability theory and fuzzy set theory, which provides a way to realize the bidirectional cognitive transformation between qualitative concept and quantitative data—forward cloud transformation and backward cloud transformation. Inspired by the cloud model theory, the realization of the bidirectional cognitive computing process in the proposed method is that the forward cloud transformation algorithm can be used to realize the cognitive transformation from intension to extension of concept, while the backward cloud transformation algorithm is to realize the cognitive transformation from extension to intension. In other words, the forward cloud transformation is a converter “from coarser to finer”, and the backward cloud transformation is a converter “from finer to coarser”. Taking some uncertain concepts as cognitive unit of simulation, several simulation experiments of the bidirectional cognition computing process are implemented in order to simulate the human cognitive process, such as cognition computing process for an uncertain concept with fixed samples, cognition computing process of dynamically giving examples, and cognition computing process of passing a concept among people. These experiment results show the validity and efficiency of the bidirectional cognitive computing model for cognition study.


Bidirectional cognitive computing model Cognitive computing Cloud model 



The authors are very grateful to the four anonymous referees for their constructive comments and suggestions in improving this paper. This work was supported by the Ningxia Natural Science Foundation (No. 2018AAC03253), the National Key Research and Development Program (No.2016YFB1000900), the National Natural Science Foundation of China (No. 61772096, 61572091), the Key Project of North Minzu University (No. ZDZX201804), the First-Class Disciplines Foundation of Ningxia (No.NXYLXK2017B09).

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. 1.
    Michael O’N. Artificial intelligence and cognitive science. Berlin: Springer; 2002.Google Scholar
  2. 2.
    Howard N, Hussain A. The fundamental code unit of the brain: towards a new model for cognitive geometry. Cogn Comput 2018;10(3):426–36.CrossRefGoogle Scholar
  3. 3.
    Wang GY. DGCC: data-driven granular cognitive computing. Granular Comput 2017;2:343–55. Scholar
  4. 4.
    Li DY, Du Y. Artificial intelligence with uncertainty, 2nd ed. London: Chapman and Hall/CRC; 2017.CrossRefGoogle Scholar
  5. 5.
    Wang GY, Xu CL, et al. Cloud model—a bidirectional cognition model between concept’s extension and intension. In: Ell Hassanien A, editor. AMLTA 2012, CCIS 322. Berlin: Springer; 2012, pp. 391–400.Google Scholar
  6. 6.
    Kanal LN, Lemmer JF. Uncertainty in artificial intelligence. New York: Elsevier Science publishing; 2008.Google Scholar
  7. 7.
    Wang GY. Rough set based uncertainty knowledge expressing and processing. In: RSFDGrC 2011. Moscow; 2011. p. 11–8.Google Scholar
  8. 8.
    Wallerstein I. The uncertainties of knowledge. Philadelphia: Temple University Press; 2004.Google Scholar
  9. 9.
    Wang ZK. Probability theory and its applications. Beijing: Beijing Normal University Press; 1995.Google Scholar
  10. 10.
    Zadeh LA. Fuzzy sets. Inf Control 1965;8(3):338–53.CrossRefGoogle Scholar
  11. 11.
    Schmucker KJ. Fuzzy sets, natural language computations, and risk analysis. Rockvill: Computer Science Press; 1984.Google Scholar
  12. 12.
    Yager RR. Uncertainty representation using fuzzy measures. IEEE Trans Syst Man Cybern B: Cybern 2002; 32(1):13–20.CrossRefGoogle Scholar
  13. 13.
    Pawlak Z. Rough sets. Int J Comput Inform Sci 1982;11(5):341–56.CrossRefGoogle Scholar
  14. 14.
    Yao YY. Interpreting concept learning in cognitive informatics and granular computing. IEEE Trans Syst Man Cybern B: Cybern 2009;39(4):855–66.CrossRefGoogle Scholar
  15. 15.
    Wille R. Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival I, editor. Ordered sets. Dordrecht-Boston: Reidel; 1982, pp. 445–70.CrossRefGoogle Scholar
  16. 16.
    Wille R. Concept lattices and conceptual knowledge systems. Comput Math Appl 1992;23:493–515.CrossRefGoogle Scholar
  17. 17.
    Ganter B, Wille R. Formal concept analysis. Germany: Springer; 1999.CrossRefGoogle Scholar
  18. 18.
    Li DY, Meng HJ, Shi XM. Membership clouds and cloud generators. J Comput Res Dev 1995;32(6):15–20.Google Scholar
  19. 19.
    Li DY, Liu CY, Gan WY. A new cognitive model: cloud model. Int J Intell Syst 2009;24:357–75.CrossRefGoogle Scholar
  20. 20.
    Ding SF, Han YZ, Yu JZ, Gu YX. A fast fuzzy support vector machine based on information granulation. Neural Comput Appl 2013;23(1):S139–44.CrossRefGoogle Scholar
  21. 21.
    Du MJ, Ding SF, Xue Y. A robust density peaks clustering algorithm using fuzzy neighborhood. Int J Mach Learn Cybern 2018;9(7):1131–40.CrossRefGoogle Scholar
  22. 22.
    Rubin SH. Computing with words. IEEE Trans Syst Man Cybern B: Cybern 1999;29(4):518–24.CrossRefGoogle Scholar
  23. 23.
    Dai ZF, Zhu H, Wen FH. Two nonparametric approaches to mean absolute deviation portfolio selection model. J Ind Manag Optim. 2019; Scholar
  24. 24.
    Chen Y, Argentinis JD E, Weber G. IBM Waston: how cognitive computing can be applied to big data challenges in life sciences research. Clin Ther 2016;38(4):688–701.PubMedCrossRefGoogle Scholar
  25. 25.
    Hu Q, Mi J, Chen D. Granular computing based machine learning in the era of big data. Inf Sci 2017;378:242–43. Scholar
  26. 26.
    Coccoli M, Maresca P, Stanganelli L. The role of big data and cognitive computing in the learning process. J Vis Lang Comput 2017;38:97–103.CrossRefGoogle Scholar
  27. 27.
    The from data to knowledge (FDK). Accessed 15 Oct 2016.
  28. 28.
    Bellinger G, Castro D, Mills A. Data, information, knowledge, and wisdom. Accessed 15 Oct 2016.
  29. 29.
    Daleiden EL, Chorpita BF. From data to wisdom: quality improvement strategies supporting large-scale implementation of evidence-based services. Child Adolesc Psychiatric Clin N Am 2005;14:329–49.CrossRefGoogle Scholar
  30. 30.
    Skowron A, Jankowski A, Dutta S. Interactive granular computing. Granul Comput 2016;1(2):95–113.CrossRefGoogle Scholar
  31. 31.
    Song ML, Wang YB. A study of granular computing in the agenda of growth of artificial neural networks. Granul Comput 2016;1(4):247–57.CrossRefGoogle Scholar
  32. 32.
    Peters G, Weber R. Dcc: a framework for dynamic granular clustering. Granul Comput 2016;1(1):1–11.CrossRefGoogle Scholar
  33. 33.
    Xu J, Wang GY, Deng WH. Denpehc: density peak based efficient hierarchical clustering. Inf Sci 2016;373:200–18.CrossRefGoogle Scholar
  34. 34.
    Chen L. Topological structure in visual perception. Science 1982;218(4573):699–700.PubMedCrossRefGoogle Scholar
  35. 35.
    Han SH, Chen L. The relationship between global properties and local properties-global precedence. Adv Psychol Sci 1996;4(1):36–41.Google Scholar
  36. 36.
    Chen L, Zhang S, Srinivasan MV. Global perception in small brains: topological pattern recognition in honey bees. Proc Natl Acad Sci 2003;100(11):6884–9.PubMedCrossRefGoogle Scholar
  37. 37.
    Zhao F, Zeng Y, Wang G, et al. A brain-inspired decision making model based on top-down biasing of prefrontal cortex to basal ganglia and its application in autonomous UAV explorations. Cogn Comput 2018;10(2):296–306.CrossRefGoogle Scholar
  38. 38.
    Li Y, Pan Q, Yang T, et al. Learning Word representations for sentiment analysis. Cogn Comput 2017;9(6):843–51.CrossRefGoogle Scholar
  39. 39.
    Ramírez-Bogantes M, Prendas-Rojas JP, Figueroa-Mata G, et al. Cognitive modeling of the natural behavior of the varroa destructor mite on video. Cogn Comput 2017;9(4):482–93.CrossRefGoogle Scholar
  40. 40.
    Wang GY, Yang J, Xu J. Granular computing: from granularity optimization to multi-granularity joint problem solving. Granul Comput 2017;2(3):105–120.CrossRefGoogle Scholar
  41. 41.
    Wang GY, Xu CL, Zhang QH, Wang XR. P-order normal cloud model recursive definition and analysis of bidirectional cognitive computing. Chin J Comput Phys 2013;36(11):2316–29.CrossRefGoogle Scholar
  42. 42.
    Wang GY, Xu CL, Li DY. Generic normal cloud model. Inf Sci 2014;280:1–15.CrossRefGoogle Scholar
  43. 43.
    Xu CL, Wang GY, Zhang QH. A new multi-step backward cloud transformation algorithm based on normal cloud model. Fund Inform 2014;133:55–85.Google Scholar
  44. 44.
    Xu CL, Wang GY. A novel cognitive transformation algorithm based on gaussian cloud model and its application in image segmentation. Numer Algorithms 2017;76(4):1039–70.CrossRefGoogle Scholar
  45. 45.
    Li DY, Liu CY. Study on the universality of the normal cloud model. Eng Sci 2004;6(8):28–34.Google Scholar
  46. 46.
    Wang SL, Li DR, Shi WZ, et al. Cloud model-based spatial data mining. Geogr Inf Sci 2003;9(2):67–78.Google Scholar
  47. 47.
    Lu HJ, Wang Y, Li DY, Liu CY. The application of backward cloud in qualitative evaluation. Chin J Comput 2003;26(8):1009–14.Google Scholar
  48. 48.
    Qin K, Xu K, Du Y, Li DY. An image segmentation approach based on histogram analysis utilizing cloud model. In: Proceedings of the 2010 seventh international conference on fuzzy systems and knowledge discovery (FSKD 2010); 2010. p. 524–8.Google Scholar
  49. 49.
    Liu CY, Feng M, Dai XJ, Li DY. A new algorithm of backward cloud. J Syst Simul 2004;16(11):2417–20.Google Scholar
  50. 50.
    Wang LX. The basic mathematical properties of normal cloud and cloud filter. Personal Communication 3. 2011.Google Scholar
  51. 51.
    Liu Y, Li DY. Statistics on atomized feature of normal cloud model. J Beijing Univ Aeronaut Astronaut 2010;36(11):1320–4.Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mathematics and Information ScienceNorth Minzu UniversityYinchuanChina
  2. 2.The Key Laboratory of Intelligent Information and Big Data Processing of NingXia ProvinceNorth Minzu UniversityYinchuanChina
  3. 3.Health Big Data Research Institute of North Minzu UniversityYinchuanChina
  4. 4.Chongqing Key Laboratory of Computational IntelligenceChongqing University of Posts and TelecommunicationsChongqingChina

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