首页|Hunan Women’s University Reports Findings in Support Vector Machines (Dynamic correction of soft measurement model for evaporation process parameters based on ARMA)
Hunan Women’s University Reports Findings in Support Vector Machines (Dynamic correction of soft measurement model for evaporation process parameters based on ARMA)
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NETL
NSTL
Amer Inst Mathematical Sciences-Aims
New research on Support Vector Machines is the subject of a report. According to news reporting out of Hunan, People’s Republic of China, by NewsRx editors, research stated, “To address the significant soft measurement errors in traditional static models for evaporation process parameters, which are characterized by continuity and cumulativity, this paper proposes a dynamic correction method for soft measurement models of evaporation process parameters based on the autoregressive moving-average model (ARMA). Initially, the Powell’s directional evolution (Powell-DE) algorithm is utilized to identify the autoregressive order and moving average order of the ARMA model.”
HunanPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningSupport Vector MachinesVector Machines
NETL
NSTL
Amer Inst Mathematical Sciences-Aims
