IEEE Trans Neural Netw Learn Syst - An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination.

Tópicos

{ system(1976) rule(880) can(841) }
{ take(945) account(800) differ(722) }
{ featur(3375) classif(2383) classifi(1994) }
{ network(2748) neural(1063) input(814) }
{ estim(2440) model(1874) function(577) }
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{ data(3963) clinic(1234) research(1004) }
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{ activ(1452) weight(1219) physic(1104) }
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{ method(1557) propos(1049) approach(1037) }
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{ data(1714) softwar(1251) tool(1186) }
{ howev(809) still(633) remain(590) }
{ perform(999) metric(946) measur(919) }
{ model(3480) simul(1196) paramet(876) }
{ signal(2180) analysi(812) frequenc(800) }
{ activ(1138) subject(705) human(624) }
{ can(774) often(719) complex(702) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ patient(2315) diseas(1263) diabet(1191) }
{ concept(1167) ontolog(924) domain(897) }
{ design(1359) user(1324) use(1319) }
{ care(1570) inform(1187) nurs(1089) }
{ state(1844) use(1261) util(961) }
{ patient(2837) hospit(1953) medic(668) }
{ health(1844) social(1437) communiti(874) }
{ use(976) code(926) identifi(902) }
{ implement(1333) system(1263) develop(1122) }
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{ studi(1410) differ(1259) use(1210) }
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{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
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{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
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{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
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{ first(2504) two(1366) second(1323) }
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{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
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{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
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Resumo

We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. The consequent part weights are tuned via the rule-ordered Kalman filter algorithm to enhance learning effectiveness. Simulation results show that IT2NFS-SIFE not only simplifies the system architecture by eliminating derogatory/irrelevant antecedent clauses, rules, and features but also maintains excellent performance.

Resumo Limpo

propos integr mechan discard derogatori featur extract fuzzi rule base interv type neural fuzzi system nfsin fact general scheme can discard bad featur irrelev anteced claus even irrelev rule highdimension input variabl larg number rule enhanc comput complex nfss also reduc interpret therefor mechan simultan extract fuzzi rule reduc impact elimin inferior featur necessari propos approach name interv type neural fuzzi system onlin system identif featur elimin itnfssif use type fuzzi set model uncertainti associ inform data design knowledg base consequ part itnfssif takagisugenokang type interv weight itnfssif possess selfevolv properti can automat generat fuzzi rule poor featur can discard concept membership modul anteced modul weight learn use gradient descent algorithm consequ part weight tune via ruleord kalman filter algorithm enhanc learn effect simul result show itnfssif simplifi system architectur elimin derogatoryirrelev anteced claus rule featur also maintain excel perform

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