{"title":"蝗虫群行进的神经模糊学习","authors":"G. Segal, A. Moshaiov, Guy Amichay, A. Ayali","doi":"10.1109/IJCNN.2016.7727335","DOIUrl":null,"url":null,"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.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Neuro-fuzzy learning of locust's marching in a Swarm\",\"authors\":\"G. Segal, A. Moshaiov, Guy Amichay, A. Ayali\",\"doi\":\"10.1109/IJCNN.2016.7727335\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":109405,\"journal\":{\"name\":\"2016 International Joint Conference on Neural Networks (IJCNN)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Joint Conference on Neural Networks (IJCNN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCNN.2016.7727335\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Joint Conference on Neural Networks (IJCNN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.2016.7727335","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neuro-fuzzy learning of locust's marching in a Swarm
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.