Fuzzy-rule emulated networks, based on reinforcement learning for nonlinear discrete-time controllers

Fuzzy-rule emulated networks, based on reinforcement learning for nonlinear discrete-time controllers

This article introduces an adaptive controller for a class of nonlinear discrete-time systems, based on self adjustable networks called Multi-Input Fuzzy Rules Emulated Networks (MIFRENs), and its reinforcement learning algorithm. Because of the universal function approximation of MIFREN, the first...

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Main Author: Chidentree Treesatayapun
Format: Journal
Published: 2018
Subjects:
Computer Science
Engineering
Mathematics
Physics and Astronomy
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=50249134186&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/60297
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-602972018-09-10T03:48:14Z Fuzzy-rule emulated networks, based on reinforcement learning for nonlinear discrete-time controllers Chidentree Treesatayapun Computer Science Engineering Mathematics Physics and Astronomy This article introduces an adaptive controller for a class of nonlinear discrete-time systems, based on self adjustable networks called Multi-Input Fuzzy Rules Emulated Networks (MIFRENs), and its reinforcement learning algorithm. Because of the universal function approximation of MIFREN, the first MIFREN called MIFRENcis used to estimate a long-term cost function, which demonstrates as a performance index for the tuning procedure. Another network or MIFRENais designed as a direct controller via the human knowledge through defined If-Then rules. The selection procedure for any system parameters, such as learning rates and some constant parameters, is represented by the proof of proposed theorems. The system's performance is demonstrated by computer simulations via selected nonlinear discrete-time systems, and comparison results with other controllers to validate theoretical development. © 2008 ISA. 2018-09-10T03:40:42Z 2018-09-10T03:40:42Z 2008-10-01 Journal 00190578 2-s2.0-50249134186 10.1016/j.isatra.2008.07.001 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=50249134186&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/60297
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Computer Science
Engineering
Mathematics
Physics and Astronomy
spellingShingle Computer Science
Engineering
Mathematics
Physics and Astronomy
Chidentree Treesatayapun
Fuzzy-rule emulated networks, based on reinforcement learning for nonlinear discrete-time controllers
description This article introduces an adaptive controller for a class of nonlinear discrete-time systems, based on self adjustable networks called Multi-Input Fuzzy Rules Emulated Networks (MIFRENs), and its reinforcement learning algorithm. Because of the universal function approximation of MIFREN, the first MIFREN called MIFRENcis used to estimate a long-term cost function, which demonstrates as a performance index for the tuning procedure. Another network or MIFRENais designed as a direct controller via the human knowledge through defined If-Then rules. The selection procedure for any system parameters, such as learning rates and some constant parameters, is represented by the proof of proposed theorems. The system's performance is demonstrated by computer simulations via selected nonlinear discrete-time systems, and comparison results with other controllers to validate theoretical development. © 2008 ISA.
format Journal
author Chidentree Treesatayapun
author_facet Chidentree Treesatayapun
author_sort Chidentree Treesatayapun
title Fuzzy-rule emulated networks, based on reinforcement learning for nonlinear discrete-time controllers
title_short Fuzzy-rule emulated networks, based on reinforcement learning for nonlinear discrete-time controllers
title_full Fuzzy-rule emulated networks, based on reinforcement learning for nonlinear discrete-time controllers
title_fullStr Fuzzy-rule emulated networks, based on reinforcement learning for nonlinear discrete-time controllers
title_full_unstemmed Fuzzy-rule emulated networks, based on reinforcement learning for nonlinear discrete-time controllers
title_sort fuzzy-rule emulated networks, based on reinforcement learning for nonlinear discrete-time controllers
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=50249134186&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/60297
_version_ 1681425409856503808

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