A hybrid genetic algorithm-extreme learning machine approach for accurate significant wave height reconstruction

Ocean Modelling

Volumn 92, Issue , 2015, Pages 115-123

  Alexandre, E ^a   Cuadra, L ^a   Nieto Borge, J C ^a   Candil Garcia G ^a   del Pino, M ^a   Salcedo Sanz, S ^a  

^a UNIVERSITY OF ALCALÁ   (Spain)

Author keywords

Extreme learning machines; Gaussian process regression; Genetic algorithm; Significant wave height local reconstruction; Support vector regression

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BUOYS; COASTAL ENGINEERING; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); GENETIC ALGORITHMS; KNOWLEDGE ACQUISITION; LEARNING ALGORITHMS; LEARNING SYSTEMS; REGRESSION ANALYSIS; WATER WAVES;

DESIGN AND OPERATIONS; EXTREME LEARNING MACHINE; GAUSSIAN PROCESS REGRESSION; HYBRID GENETIC ALGORITHMS; RECONSTRUCTION ERROR; SIGNIFICANT WAVE HEIGHT; SUPPORT VECTOR REGRESSION (SVR); WAVE ENERGY CONVERTERS;

WAVE ENERGY CONVERSION;

ARTIFICIAL INTELLIGENCE; GAUSSIAN METHOD; GENETIC ALGORITHM; RECONSTRUCTION; REGRESSION ANALYSIS; SIGNIFICANT WAVE HEIGHT; WAVE ENERGY;

ATLANTIC OCEAN; ATLANTIC OCEAN (WEST); CARIBBEAN SEA;

EID: 84937002395     PISSN: 14635003     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ocemod.2015.06.010     Document Type: Article

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