IEEE Trans Neural Netw Learn Syst - GrDHP: a general utility function representation for dual heuristic dynamic programming.

Tópicos

{ state(1844) use(1261) util(961) }
{ model(2341) predict(2261) use(1141) }
{ system(1050) medic(1026) inform(1018) }
{ network(2748) neural(1063) input(814) }
{ can(981) present(881) function(850) }
{ method(1219) similar(1157) match(930) }
{ learn(2355) train(1041) set(1003) }
{ model(3404) distribut(989) bayesian(671) }
{ take(945) account(800) differ(722) }
{ record(1888) medic(1808) patient(1693) }
{ group(2977) signific(1463) compar(1072) }
{ process(1125) use(805) approach(778) }
{ can(774) often(719) complex(702) }
{ design(1359) user(1324) use(1319) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ implement(1333) system(1263) develop(1122) }
{ method(1969) cluster(1462) data(1082) }
{ imag(1947) propos(1133) code(1026) }
{ inform(2794) health(2639) internet(1427) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ control(1307) perform(991) simul(935) }
{ risk(3053) factor(974) diseas(938) }
{ spatial(1525) area(1432) region(1030) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ patient(2837) hospit(1953) medic(668) }
{ sampl(1606) size(1419) use(1276) }
{ intervent(3218) particip(2042) group(1664) }
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{ data(1737) use(1416) pattern(1282) }
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{ measur(2081) correl(1212) valu(896) }
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{ featur(3375) classif(2383) classifi(1994) }
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{ patient(2315) diseas(1263) diabet(1191) }
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{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
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{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
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{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ health(3367) inform(1360) care(1135) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

A general utility function representation is proposed to provide the required derivable and adjustable utility function for the dual heuristic dynamic programming (DHP) design. Goal representation DHP (GrDHP) is presented with a goal network being on top of the traditional DHP design. This goal network provides a general mapping between the system states and the derivatives of the utility function. With this proposed architecture, we can obtain the required derivatives of the utility function directly from the goal network. In addition, instead of a fixed predefined utility function in literature, we conduct an online learning process for the goal network so that the derivatives of the utility function can be adaptively tuned over time. We provide the control performance of both the proposed GrDHP and the traditional DHP approaches under the same environment and parameter settings. The statistical simulation results and the snapshot of the system variables are presented to demonstrate the improved learning and controlling performance. We also apply both approaches to a power system example to further demonstrate the control capabilities of the GrDHP approach.

Resumo Limpo

general util function represent propos provid requir deriv adjust util function dual heurist dynam program dhp design goal represent dhp grdhp present goal network top tradit dhp design goal network provid general map system state deriv util function propos architectur can obtain requir deriv util function direct goal network addit instead fix predefin util function literatur conduct onlin learn process goal network deriv util function can adapt tune time provid control perform propos grdhp tradit dhp approach environ paramet set statist simul result snapshot system variabl present demonstr improv learn control perform also appli approach power system exampl demonstr control capabl grdhp approach

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