A new computational method for solving fully fuzzy nonlinear systems

Raheleh Jafari, Sina Razvarz, Alexander Gegov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Predicting the solution of complex systems is a significant challenge. Complexity is caused mainly by uncertainty and nonlinearity. The nonlinear nature of many complex systems leaves uncertainty irreducible in many cases. In this work, a novel iterative strategy based on the feedback neural network is recommended to obtain the approximated solutions of the fully fuzzy nonlinear system (FFNS). In order to obtain the estimated solutions, a gradient descent algorithm is suggested for training the feedback neural network. An example is laid down in order to demonstrate the high accuracy of this suggested technique.
Original languageEnglish
Title of host publicationComputational Collective Intelligence
Subtitle of host publication10th International Conference, ICCCI 2018, Bristol, UK, September 5-7, 2018, Proceedings, Part I
EditorsNgoc Thanh Nguyen, Elias Pimenidis, Zaheer Khan, Bogdan Trawiński
ISBN (Electronic)978-3-319-98443-8
ISBN (Print)978-3-319-98442-1
Publication statusPublished - Sept 2018
Event10th International Conference on Collective Intelligence - Bristol, United Kingdom
Duration: 5 Sept 20187 Sept 2018

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th International Conference on Collective Intelligence
Abbreviated titleICCCI 2018
Country/TerritoryUnited Kingdom


  • approximate solution
  • complex system
  • feedback neural network
  • gradient descent algorithm
  • simulation


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