Modeling of turbine cycles using a neuro-fuzzy based approach to predict turbine-generator output for nuclear power plants

Energies

Volumn 5, Issue 1, 2012, Pages 101-118

  Chan, Yea Kuang ^a,b   Gu, Jyh Cherng ^a  

^a NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b INSTITUTE OF NUCLEAR ENERGY RESEARCH   (Taiwan)

Author keywords

Adaptive neuro fuzzy inference system (ANFIS); Neural network; Nuclear power plant; Turbine cycle; Turbine generator

Indexed keywords

DEEP NEURAL NETWORKS; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; LINEAR REGRESSION; NEURAL NETWORKS; NUCLEAR ENERGY; NUCLEAR FUELS; NUCLEAR POWER PLANTS; REGRESSION ANALYSIS; TRACKING (POSITION); TURBINES; TURBOGENERATORS;

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; COMMERCIAL SOFTWARE; CONFIDENCE INTERVAL; LINEAR REGRESSION MODELS; PLANT OPERATION DATA; THERMAL PERFORMANCE; THERMODYNAMIC PROCESS; TURBINE CYCLE;

FUZZY INFERENCE;

EID: 84859304577     PISSN: None     EISSN: 19961073     Source Type: Journal    
DOI: 10.3390/en5010101     Document Type: Article

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