Advertisement

Model for Forecasting Yields Under Fuzzy Initial Conditions

  • T. F. Bekmuratov
  • N. A. Niyozmatova
Conference paper

Abstract

The problems of constructing a forecasting model for yields under indistinctly specified information on the climatic and agrotechnical conditions of growing agricultural crops have been considered. A yield forecasting model based on fuzzy models of the Sugeno type has been described. The model and rules of fuzzy inference have been presented in an unclear knowledge base in the form of an expert matrix of knowledge. An algorithm for constructing a prediction model of this type has been given. The organization of a computational experiment has been described, according to the effectiveness of the proposed model for forecasting the yield of cotton.

Keywords

Forecasting model Fuzzy set Product rules Fuzzy conclusion Fuzzy model Knowledge base Expert knowledge matrix Algorithm Alternative Decision making 

References

  1. 1.
    Zadeh LA (1969) Toword a theory of fuzzy systems. ERL Rep. № 69-2. Elektronics Res. Labs., Univ. California, BerkleyGoogle Scholar
  2. 2.
    Bellman RE, Zadeh LA (1970) Decision-making in fuzzy environment. Manag Sci 17(4):141–164MathSciNetCrossRefGoogle Scholar
  3. 3.
    Borisov AN (1989) Processing of fuzzy information in decision-making systems. In: Alekseev AV, Merkuriev GV (eds) Radio and communication, vol 304. Radio i sviaz, MoscowGoogle Scholar
  4. 4.
    Aliyev RA (2001) The theory of intelligent systems and its application. Chashyogly, Baku. 720pGoogle Scholar
  5. 5.
    Bekmuratov TF (2008) Poorly structured decision – making in problems of management of risks. In: Proceedings of the 5th world conference on intelligent systems for industrial automation, b – Quadrat Verlag. Tashkent (Uzbekistan), November 25–27, pp 96−106Google Scholar
  6. 6.
    Bekmuratov ТF, Мukhamedieva DT (2008) Decision-making problem in poorly formalized processes. In: Proceedingsof the 5th world conference on intelligent systems for industrial automation, b – Quadrat Verlag Tashkent (Uzbekistan), November 25−27, pp 214−218Google Scholar
  7. 7.
    Bekmuratov TF, Dadabaeva RA, Mukhamedieva DT (2010) Decision-making in a fuzzy environment. Probl Inform (Novosibirsk) 1:52–60Google Scholar
  8. 8.
    Shtovba SD. Introduction to the theory of fuzzy sets and fuzzy logic. http://www.matlab.exponenta.ru
  9. 9.
    Mukhamedieva DT (2014) Development of fuzzy models of decision-making tasks. Publishing house. Palmarium Academic Publishing/AV Akademikerverlag GmbH & Co.KG Heinrich-Böcking-Str, 6–8, 66121 Saarbrucken, Germany. 190pGoogle Scholar
  10. 10.
    Mukhamedieva DT (2014) Application of soft calculation methods in weakly formalized systems. Publishing house “Palmarium Academic Publishing”/AV Akademikerverlag GmbH & Co.KG Heinrich-Böcking-Str, 6–8, 66121 Saarbrucken, Germany. 181pGoogle Scholar
  11. 11.
    Mukhamedieva DT (2017) Intellectual analysis of fuzzy solutions to noncorrect problems. Palmarium Academic Publishing/AV Akademikerverlag GmbH & Co.KG Heinrich-Böcking-Str, 6–8, 66121 Saarbrucken, Germany. 327pGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • T. F. Bekmuratov
    • 1
  • N. A. Niyozmatova
    • 1
  1. 1.Scientific and Innovation Center of Information and Communication TechnologiesTashkentUzbekistan

Personalised recommendations