Advertisement

Construction of the Model of Crop Production Forecasting with Fuzzy Information

  • Dilnoz Mukhamediyeva
  • Barnoxon Solieva
Conference paper

Abstract

The model of the yield forecast, realized as fuzzy knowledge bases, has been described. An algorithm for constructing a prediction model of this type has been given. In the basic procedures of this algorithm, operations of fuzzy inference have been used. A lot of inference rules describing the forecast model appear in a fuzzy knowledge base in the form of an expert knowledge matrix. The organization of a computational experiment on the evaluation of the effectiveness of the proposed model for predicting cotton yields is outlined. The input parameters of the model under study are: weather (climatic) conditions during sowing, vegetation and harvesting, degree of water supply, types of crops, types of soils, and types and amount of fertilizer application. The problems of constructing a forecasting model for yields under indistinctly specified information on the climatic and agro-technical conditions of growing agricultural crops were considered.

Keywords

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

References

  1. 1.
    Zade L (1974) Foundations of a new approach to the analysis of complex systems and decision-making processes. In: Mathematics today. Knowledge, Moskow, pp 5–49Google Scholar
  2. 2.
    Bellman R, Zade L (1976) Decision-making under vague conditions. Analysis questions and decision-making procedures. Mir, Moskow, pp 172–215Google Scholar
  3. 3.
    Borisov AN (1989) In: Alekseev AV, Merkuriev GV (eds) Processing of fuzzy information in decision-making systems. Radio and Communication, Moskow, p 304zbMATHGoogle Scholar
  4. 4.
    Aliev RA, Aliev RA (2001) The theory of intellectual systems and its application. Chashyogly, Baku, p 720Google Scholar
  5. 5.
    Bekmuratov TF (2005) Fuzzy models of problems of decision support during monitoring in conditions of uncertainty. Problems of Informatics and Energy 3:9–18Google Scholar
  6. 6.
    Mukhamedieva DT (2007) Modeling of weakly formalized processes on the basis of processing of fuzzy information. Institute of Informatics of the Academy of Sciences of Uzbekistan, Tashkent, p 231Google Scholar
  7. 7.
    Mukhamedieva DT (2012) Development of fuzzy models of forecasting and optimization problems. Publishing house “Fan va Technology”, Tashkent, p 346Google Scholar
  8. 8.
    Mukhamedieva DT (2013) Algorithm of clustering rules of systems of fuzzy inference. Natural and Technical Sciences 2:248–252Google Scholar
  9. 9.
    Shtovba SD Introduction to the theory of fuzzy sets and fuzzy logic. Access mode: http://www.matlab.exponenta.ru, free
  10. 10.
    Rotshtein AP (1999) Intellectual identification technologies: fuzzy logic, genetic algorithms, neural networks. UNIVERSUM-Vinnitsa, Vinnitsa, p 320Google Scholar
  11. 11.
    Rutkovskaya D, Pilinsky M, Rutkowski L (2006) Neural networks, genetic algorithms and fuzzy systems (trans: from Polish Rudinsky ID). Hot line – Telecom, MoskowGoogle Scholar
  12. 12.
    Katasev AS, Akhatova Ch F (2010) Neuroneutchek model of formation of knowledge bases of expert systems with genetic learning algorithm. Problems of control and modeling in complex systems: tr. XII Intern. Conf. Samara: Samar. Sci. Center of the Russian Academy of Sciences, Samara, pp 615–621Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Dilnoz Mukhamediyeva
    • 1
  • Barnoxon Solieva
    • 1
  1. 1.Scientific and Innovation Center of Information and Communication TechnologiesTashkentUzbekistan

Personalised recommendations