Neuro-fuzzy learning of locust's marching in a Swarm

Gil Segal, Amiram Moshaiov, Guy Amichay, Amir Ayali

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This study deals with the identification of the behavior of an individual in a group of marching locusts, as observed under laboratory conditions. In particular, the study focuses on the intermittent motion (walking initiation and pausing) of the locusts using Adaptive Neuro-Fuzzy Inference System (ANFIS). Several possible fuzzy rules were examined in a trial-and-error approach, before establishing a reliable set of rules. Analysis of this set led to a consequent reduced fuzzy controller. The results of this study serve as a first step towards achieving the long-term goal of understanding how the behavior of an individual locust translates to the collective swarm movement. As part of achieving this goal, we plan on building a locust-like robot and investigating its behavior within a living swarm of locusts. On a more general level, this study demonstrates, for the first time, that ANFIS can be used to support the understanding of biological systems by translating experimental data into meaningful control laws.

Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks, IJCNN 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1208-1215
Number of pages8
ISBN (Electronic)9781509006199
DOIs
StatePublished - 31 Oct 2016
Event2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2016-October

Conference

Conference2016 International Joint Conference on Neural Networks, IJCNN 2016
Country/TerritoryCanada
CityVancouver
Period24/07/1629/07/16

Keywords

  • ANFIS
  • Artificial life
  • Fuzzy control
  • Locust
  • Swarm
  • System identification

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