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A Neuro-Fuzzy Pricing Model in Conditions of Market Uncertainty

  • N. Yu. MutovkinaEmail author
  • A. N. Borodulin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1126)

Abstract

The article proposes a pricing model in the face of uncertainty at an early stage in the product life cycle. The development of new products, products, and design solutions is characterizing by the lack of the necessary amount of value and physical indicators for accurate pricing. At the early stage of the product life cycle, it is possible to operate only with its intended function or consumer properties. The number of statistical data on the demand for such a product on the market is severely limiting due to its novelty. Therefore, data on the results of sales of products with similar characteristics are taking as the initial statistical sample. The model is basing on expert assessment methods and the construction of hybrid neural networks. The analysis of the retrospective data is carried out taking into account the expert’s ideas about the features of the system. The simulation was performing in the MATLAB software environment with the Fuzzy Logic Toolbox expansion pack.

Keywords

Pricing Hybrid neural networks Expert assessments Membership functions ANFIS editor Fuzzy inference 

Notes

Acknowledgments

The reported study was funded by RFBR according to the research project No. 17-01-00817A.

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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Tver State Technical UniversityTverRussia

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