01168nam--2200397---450-99000080159020331620070413114230.03-598-11182-70080159USA010080159(ALEPH)000080159USA01008015920011210d1995----km-y0itay0103----baengDE||||||||001yyMicroform maket place, 1994-1995an international directory of micropublishingeditor Barbara hopkinsonMüchenK.G. Saur1995VIII, 227 p.21 cm2001001-------2001MicoformeBibliografie011.36HOPKINSON,BarbaraITsalbcISBD990000801590203316XV B 534119783 LMXV BBKUMAPATTY9020011210USA01120720020403USA011726PATRY9020040406USA011655COPAT19020050711USA011001COPAT59020070413USA011142Microform maket place, 1994-1995966395UNISA02002oam 2200433zu 450 991013915620332120241212215856.097807695428120769542816(CKB)2560000000059180(SSID)ssj0000527489(PQKBManifestationID)12208042(PQKBTitleCode)TC0000527489(PQKBWorkID)10526399(PQKB)11661470(NjHacI)992560000000059180(EXLCZ)99256000000005918020160829d2010 uy engur|||||||||||txtccr2010 Fourth International Conference on Genetic and Evolutionary Computing[Place of publication not identified]I E E E20101 online resourceBibliographic Level Mode of Issuance: Monograph9781424488919 1424488915 Coal or rock electromagnetic emission analysis is a promising method for predicting coal or rock dynamic disasters. Hidden Markov Model (HMM) is applied to this problem in this paper. HMM model is a processing method of dynamic information based on probability, which can reflect both randomicity and potential structure of the object. Model selecting of HMM Bayes Information Criterion is combined with classical optimization algorithm. Moreover, k-means clustering algorithm and Gaussian mixture model are introduced to initial HMM model. Researches in this paper indicate that HMM is an outstanding probability learning model which can perfectly analyse coal or rock electromagnetic emission time series problems.Evolutionary computationCongressesEvolutionary computation005.432IEEE StaffPQKBPROCEEDING99101391562033212010 Fourth International Conference on Genetic and Evolutionary Computing2349678UNINA