Using self-adaptive evolutionary algorithm to improve the performance of an extreme learning machine for estimating soil temperature

Computers and Electronics in Agriculture

Volumn 124, Issue , 2016, Pages 150-160

  Nahvi, Behnaz ^a   Habibi, Jafar ^b   Mohammadi, Kasra ^c   Shamshirband, Shahaboddin ^d   Al Razgan, Othman Saleh ^e  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

^b SHARIF UNIVERSITY OF TECHNOLOGY   (Iran)

^c University of Massachusetts   (United States)

^d UNIVERSITY OF MALAYA   (Malaysia)

^e KUWAIT INSTITUTE FOR SCIENTIFIC RESEARCH   (Kuwait)

Author keywords

Agent; Estimation; Extreme Learning Machine (ELM); Self Adaptive Evolutionary Extreme Learning Machine (SaE ELM); Soil temperature

Indexed keywords

AGENTS; ALGORITHMS; ATMOSPHERIC PRESSURE; ATMOSPHERIC TEMPERATURE; ESTIMATION; EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHMS; GENETIC PROGRAMMING; KNOWLEDGE ACQUISITION; NEURAL NETWORKS; SOILS; TEMPERATURE;

ARCHITECTURE-BASED; ARTIFICIAL NEURAL NETWORK MODELS; AVERAGE AIR TEMPERATURE; CORRELATION COEFFICIENT; EXTREME LEARNING MACHINE; GLOBAL SOLAR RADIATION; LOGICAL PROCEDURES; SOIL TEMPERATURE;

LEARNING SYSTEMS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; ATMOSPHERIC PRESSURE; CLIMATE CONDITIONS; ESTIMATION METHOD; PERFORMANCE ASSESSMENT; PRECISION; SOIL TEMPERATURE; SOLAR RADIATION;

BANDAR ABBAS; HORMOZGAN; IRAN; KERMAN [IRAN];

EID: 84962881154     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2016.03.025     Document Type: Article

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