Direct Inference Based on Fuzzy Rules

  • Alexander P. Rotshtein
  • Hanna B. Rakytyanska
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 275)


This chapter is devoted to the methodology aspects of identification and decision making on the basis of intellectual technologies. The essence of intellectuality consists of representation of the structure of the object in the form of linguistic IF-THEN rules, reflecting human reasoning on the common sense and practical knowledge level. The linguistic approach to designing complex systems based on linguistically described models was originally initiated by Zadeh [1] and developed further by Tong [2], Gupta [3], Pedrych [4 – 6], Sugeno [7], Yager [8], Zimmermann [9], Kacprzyk [10], Kandel [11]. The main principles of fuzzy modeling were formulated by Yager [8]. The linguistic model is a knowledge-based system. The set of fuzzy IF-THEN rules takes the place of the usual set of equations used to characterize a system [12 – 14]. The fuzzy sets associated with input and output variables are the parameters of the linguistic model [15]; the number of the rules determines its structure. Different interpretations of the knowledge contained in these rules, which are due to different reasoning mechanisms, result in different types of models.


Membership Function Fuzzy Rule Linguistic Term Continuous Output Direct Inference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Alexander P. Rotshtein
    • 1
  • Hanna B. Rakytyanska
    • 2
  1. 1.Department of Industrial Engineering and ManagementJerusalem College of Technology - Lev InstituteJerusalemIsrael
  2. 2.Department of Software DesignVinnytsia National Technical UniversityVinnitsaUkraine

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