Applying brain emotional learning algorithm for multivariable control of HVAC systems

In this paper, we apply a modified version of Brain Emotional Learning (BEL) controller for Heating, Ventilating and Air Conditioning (HVAC) control system whose multivariable, nonlinear and non-minimum phase nature makes the task difficult. The proposed biologically-motivated algorithm achieves rob...

Full description

Saved in:
Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2006-01, Vol.17 (1), p.35-46
Main Authors: Sheikholeslami, N, Shahmirzadi, D, Semsar, E, Lucas, C, Yazdanpanah, MJ
Format: Article
Language:English
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 46
container_issue 1
container_start_page 35
container_title Journal of intelligent & fuzzy systems
container_volume 17
creator Sheikholeslami, N
Shahmirzadi, D
Semsar, E
Lucas, C
Yazdanpanah, MJ
description In this paper, we apply a modified version of Brain Emotional Learning (BEL) controller for Heating, Ventilating and Air Conditioning (HVAC) control system whose multivariable, nonlinear and non-minimum phase nature makes the task difficult. The proposed biologically-motivated algorithm achieves robust and satisfactory performance even though there are more than one control inputs to the plant, which may be used to get the desired performance. The response time is also very fast despite the fact that the control strategy is based on satisficing decision making. The proposed strategy is very flexible and alternative performance specifications can easily be enforced via defining proper emotional cues. Simulation results reveal the effectiveness of the approach.
format article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_miscellaneous_19350215</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>19350215</sourcerecordid><originalsourceid>FETCH-LOGICAL-p186t-1b2da4c36531d804e9654706dbf9ba21e8c31e82cc8491edd8d0bfe28d90b9973</originalsourceid><addsrcrecordid>eNotjE9LwzAcQHNQcE6_Q07eCvnXNDmWok4YeNm8jqT5dUbSpCapsG-vopf3Dg_eFdpQIkVDmZA36LaUD0Jo1zKyQcd-WcLFxzO22fiIYU7Vp2gCDmBy_A0mnFP29X3GU8p4XkP1XyZ7YwPgMcWaU8Bpwru3fsDlUirM5Q5dTyYUuP_3Fh2fHg_Drtm_Pr8M_b5ZqJK1oZY5I0YuW06dIgK0bEVHpLOTtoZRUCP_ARtHJTQF55QjdgKmnCZW645v0cPfd8npc4VST7MvI4RgIqS1nKjmLWG05d-RZk5t</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>19350215</pqid></control><display><type>article</type><title>Applying brain emotional learning algorithm for multivariable control of HVAC systems</title><source>Business Source Ultimate</source><creator>Sheikholeslami, N ; Shahmirzadi, D ; Semsar, E ; Lucas, C ; Yazdanpanah, MJ</creator><creatorcontrib>Sheikholeslami, N ; Shahmirzadi, D ; Semsar, E ; Lucas, C ; Yazdanpanah, MJ</creatorcontrib><description>In this paper, we apply a modified version of Brain Emotional Learning (BEL) controller for Heating, Ventilating and Air Conditioning (HVAC) control system whose multivariable, nonlinear and non-minimum phase nature makes the task difficult. The proposed biologically-motivated algorithm achieves robust and satisfactory performance even though there are more than one control inputs to the plant, which may be used to get the desired performance. The response time is also very fast despite the fact that the control strategy is based on satisficing decision making. The proposed strategy is very flexible and alternative performance specifications can easily be enforced via defining proper emotional cues. Simulation results reveal the effectiveness of the approach.</description><identifier>ISSN: 1064-1246</identifier><language>eng</language><ispartof>Journal of intelligent &amp; fuzzy systems, 2006-01, Vol.17 (1), p.35-46</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781</link.rule.ids></links><search><creatorcontrib>Sheikholeslami, N</creatorcontrib><creatorcontrib>Shahmirzadi, D</creatorcontrib><creatorcontrib>Semsar, E</creatorcontrib><creatorcontrib>Lucas, C</creatorcontrib><creatorcontrib>Yazdanpanah, MJ</creatorcontrib><title>Applying brain emotional learning algorithm for multivariable control of HVAC systems</title><title>Journal of intelligent &amp; fuzzy systems</title><description>In this paper, we apply a modified version of Brain Emotional Learning (BEL) controller for Heating, Ventilating and Air Conditioning (HVAC) control system whose multivariable, nonlinear and non-minimum phase nature makes the task difficult. The proposed biologically-motivated algorithm achieves robust and satisfactory performance even though there are more than one control inputs to the plant, which may be used to get the desired performance. The response time is also very fast despite the fact that the control strategy is based on satisficing decision making. The proposed strategy is very flexible and alternative performance specifications can easily be enforced via defining proper emotional cues. Simulation results reveal the effectiveness of the approach.</description><issn>1064-1246</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><recordid>eNotjE9LwzAcQHNQcE6_Q07eCvnXNDmWok4YeNm8jqT5dUbSpCapsG-vopf3Dg_eFdpQIkVDmZA36LaUD0Jo1zKyQcd-WcLFxzO22fiIYU7Vp2gCDmBy_A0mnFP29X3GU8p4XkP1XyZ7YwPgMcWaU8Bpwru3fsDlUirM5Q5dTyYUuP_3Fh2fHg_Drtm_Pr8M_b5ZqJK1oZY5I0YuW06dIgK0bEVHpLOTtoZRUCP_ARtHJTQF55QjdgKmnCZW645v0cPfd8npc4VST7MvI4RgIqS1nKjmLWG05d-RZk5t</recordid><startdate>20060101</startdate><enddate>20060101</enddate><creator>Sheikholeslami, N</creator><creator>Shahmirzadi, D</creator><creator>Semsar, E</creator><creator>Lucas, C</creator><creator>Yazdanpanah, MJ</creator><scope>7TK</scope></search><sort><creationdate>20060101</creationdate><title>Applying brain emotional learning algorithm for multivariable control of HVAC systems</title><author>Sheikholeslami, N ; Shahmirzadi, D ; Semsar, E ; Lucas, C ; Yazdanpanah, MJ</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p186t-1b2da4c36531d804e9654706dbf9ba21e8c31e82cc8491edd8d0bfe28d90b9973</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sheikholeslami, N</creatorcontrib><creatorcontrib>Shahmirzadi, D</creatorcontrib><creatorcontrib>Semsar, E</creatorcontrib><creatorcontrib>Lucas, C</creatorcontrib><creatorcontrib>Yazdanpanah, MJ</creatorcontrib><collection>Neurosciences Abstracts</collection><jtitle>Journal of intelligent &amp; fuzzy systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sheikholeslami, N</au><au>Shahmirzadi, D</au><au>Semsar, E</au><au>Lucas, C</au><au>Yazdanpanah, MJ</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Applying brain emotional learning algorithm for multivariable control of HVAC systems</atitle><jtitle>Journal of intelligent &amp; fuzzy systems</jtitle><date>2006-01-01</date><risdate>2006</risdate><volume>17</volume><issue>1</issue><spage>35</spage><epage>46</epage><pages>35-46</pages><issn>1064-1246</issn><abstract>In this paper, we apply a modified version of Brain Emotional Learning (BEL) controller for Heating, Ventilating and Air Conditioning (HVAC) control system whose multivariable, nonlinear and non-minimum phase nature makes the task difficult. The proposed biologically-motivated algorithm achieves robust and satisfactory performance even though there are more than one control inputs to the plant, which may be used to get the desired performance. The response time is also very fast despite the fact that the control strategy is based on satisficing decision making. The proposed strategy is very flexible and alternative performance specifications can easily be enforced via defining proper emotional cues. Simulation results reveal the effectiveness of the approach.</abstract><tpages>12</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1064-1246
ispartof Journal of intelligent & fuzzy systems, 2006-01, Vol.17 (1), p.35-46
issn 1064-1246
language eng
recordid cdi_proquest_miscellaneous_19350215
source Business Source Ultimate
title Applying brain emotional learning algorithm for multivariable control of HVAC systems
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-19T09%3A40%3A14IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Applying%20brain%20emotional%20learning%20algorithm%20for%20multivariable%20control%20of%20HVAC%20systems&rft.jtitle=Journal%20of%20intelligent%20&%20fuzzy%20systems&rft.au=Sheikholeslami,%20N&rft.date=2006-01-01&rft.volume=17&rft.issue=1&rft.spage=35&rft.epage=46&rft.pages=35-46&rft.issn=1064-1246&rft_id=info:doi/&rft_dat=%3Cproquest%3E19350215%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-p186t-1b2da4c36531d804e9654706dbf9ba21e8c31e82cc8491edd8d0bfe28d90b9973%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=19350215&rft_id=info:pmid/&rfr_iscdi=true