Abstract
Neural networks have proven themselves in solving problems when the input and output data are known, but the cause and effect relationship between them is not obvious. A well-trained neural network will find the right answer to a given request, but will not give any idea about the rules that form this data. The paper proposes an algorithm for constructing logical operations, in terms of multi-valued logic, to identify hidden patterns in poorly formalized areas of knowledge. As the basic elements are considered many functions of the multi-valued logic of generalized addition and multiplication. The combination of these functions makes it possible to detect relationships in the data under study, as well as the ability to correct the results of neural networks. The proposed approach was considered for classification problems, in the case of multidimensional discrete features, where each feature can take k-different values and is equivalent in importance to class identification.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Flach, P.: Machine Learning: The Art and Science of Algorithms that Make Sense of Data, p. 396. Cambridge University Press, Cambridge (2012)
Yablonsky, S.V.: Introduction to Discrete Mathematics, p. 384. Mir Publishers, Moscow (2008)
Voroncov, K.V.: Optimizacionnye metody linejnoj i monotonnoj korrekcii v alge-braicheskom podhode k probleme raspoznavanija. Zhurnal vychislitel’noj ma-tematiki i matematicheskoj fiziki. T. 40(1), 166–176 (2000)
Lyutikova, L.A., Shmatova, E.V.: Application of variable-valued logic to correct pattern recognition algorithms. In: Advances in Intelligent Systems and Computing, vol. 948, pp. 308–314 (2020)
Shibzukhov, Z.M.: Aggregation correct operations on algorithms. Dokl. Math. T. 91(3), 391–393 (2015)
Acknowledgement
The reported study was funded by RFBR according to the research project № 18-01-00050-a.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lyutikova, L.A., Shmatova, E.V. (2021). Algorithm for Constructing Logical Operations to Identify Patterns in Data. In: Samsonovich, A.V., Gudwin, R.R., Simões, A.d.S. (eds) Brain-Inspired Cognitive Architectures for Artificial Intelligence: BICA*AI 2020. BICA 2020. Advances in Intelligent Systems and Computing, vol 1310. Springer, Cham. https://doi.org/10.1007/978-3-030-65596-9_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-65596-9_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-65595-2
Online ISBN: 978-3-030-65596-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)