Adaptive critic learning with fuzzy utility
Adaptive critic methods, which approximate dynamic programming, have been used successfully for solving optimal control problems. The adaptive critic learning algorithm optimizes a secondary utility function that is the sum of the present and all future primary utility. The primary utility function...
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description | Adaptive critic methods, which approximate dynamic programming, have been used successfully for solving optimal control problems. The adaptive critic learning algorithm optimizes a secondary utility function that is the sum of the present and all future primary utility. The primary utility function measures the instantaneous cost incurred for the last action taken and the resulting state. The motivation for using a fuzzy primary utility function comes from the set of control problems for which there is only a qualitative definition of performance - for example, success or failure. Previous work in applying adaptive critic methods to this type of problem showed that a crisp definition of success resulted in solutions that met the control objective, but in an undesirable manner. An appropriate fuzzy utility function, on the other hand, is able to generate the optimal solution. Another motivation for incorporating fuzzy techniques into the utility function is to overcome measurement noise. Measurement noise has a significant adverse effect on the reliability and speed of adaptive critic learning; by incorporating fuzzy sets into the utility function, the effect of the noise can be mitigated. |
doi_str_mv | 10.1109/NAFIPS.2004.1337421 |
format | Conference Proceeding |
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The adaptive critic learning algorithm optimizes a secondary utility function that is the sum of the present and all future primary utility. The primary utility function measures the instantaneous cost incurred for the last action taken and the resulting state. The motivation for using a fuzzy primary utility function comes from the set of control problems for which there is only a qualitative definition of performance - for example, success or failure. Previous work in applying adaptive critic methods to this type of problem showed that a crisp definition of success resulted in solutions that met the control objective, but in an undesirable manner. An appropriate fuzzy utility function, on the other hand, is able to generate the optimal solution. Another motivation for incorporating fuzzy techniques into the utility function is to overcome measurement noise. 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Measurement noise has a significant adverse effect on the reliability and speed of adaptive critic learning; by incorporating fuzzy sets into the utility function, the effect of the noise can be mitigated.</description><subject>Adaptive control</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computational intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Control systems</subject><subject>Cost function</subject><subject>Dynamic programming</subject><subject>Exact sciences and technology</subject><subject>Fuzzy sets</subject><subject>Neural networks</subject><subject>Noise measurement</subject><subject>Optimal control</subject><subject>Programmable control</subject><isbn>9780780383760</isbn><isbn>0780383761</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2004</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFj0FLxDAUhAMiKGt_wV568SSt7_UlbXIsi6sLiwrqeUnTV43UUtqs0v31Fio4DMxhPgZGiDVCigjm9rHc7p5f0gxApkhUyAzPRGQKDbNJU5HDhYjG8RNmSakU0qW4KWvbB__NsRt88C5u2Q6d797jHx8-4uZ4Ok3xMfjWh-lKnDe2HTn6y5V42969bh6S_dP9blPuE4-oQ8I5VGCyCkgakk7pQrEiaxhkzhlbVFQpylxjWecSiiq3VBtGVjVrZ5BW4nrZ7e3obNsMtnN-PPSD_7LDdMD5js6Nmrn1wnlm_q-X6_QLvX9N5g</recordid><startdate>2004</startdate><enddate>2004</enddate><creator>Matzner, S.A.</creator><creator>Shannon, T.T.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope><scope>IQODW</scope></search><sort><creationdate>2004</creationdate><title>Adaptive critic learning with fuzzy utility</title><author>Matzner, S.A. ; Shannon, T.T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i118t-e60b092b034934c5875e53a9e046e2ea153b532cfae86407b6a3d9e1e5de8c913</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Adaptive control</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computational intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Control systems</topic><topic>Cost function</topic><topic>Dynamic programming</topic><topic>Exact sciences and technology</topic><topic>Fuzzy sets</topic><topic>Neural networks</topic><topic>Noise measurement</topic><topic>Optimal control</topic><topic>Programmable control</topic><toplevel>online_resources</toplevel><creatorcontrib>Matzner, S.A.</creatorcontrib><creatorcontrib>Shannon, T.T.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Matzner, S.A.</au><au>Shannon, T.T.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Adaptive critic learning with fuzzy utility</atitle><btitle>IEEE Annual Meeting of the Fuzzy Information, 2004. 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Previous work in applying adaptive critic methods to this type of problem showed that a crisp definition of success resulted in solutions that met the control objective, but in an undesirable manner. An appropriate fuzzy utility function, on the other hand, is able to generate the optimal solution. Another motivation for incorporating fuzzy techniques into the utility function is to overcome measurement noise. Measurement noise has a significant adverse effect on the reliability and speed of adaptive critic learning; by incorporating fuzzy sets into the utility function, the effect of the noise can be mitigated.</abstract><cop>Piscataway NJ</cop><pub>IEEE</pub><doi>10.1109/NAFIPS.2004.1337421</doi><tpages>5</tpages></addata></record> |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Adaptive control Applied sciences Artificial intelligence Computational intelligence Computer science control theory systems Control systems Cost function Dynamic programming Exact sciences and technology Fuzzy sets Neural networks Noise measurement Optimal control Programmable control |
title | Adaptive critic learning with fuzzy utility |
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