Novel neural network-based prediction model for quantifying hydroquinone in compost with biosensor measurements

Environmental Engineering Science

Volumn 26, Issue 6, 2009, Pages 1063-1070

  Zhang, Yi ^a   Li, Wen Wei ^b   Zeng, Guang Ming ^a   Tang, Lin ^a   Feng, Chong Ling ^a   Huang, Dan Lian ^a   Li, Yuan Ping ^a  

^a HUNAN UNIVERSITY   (China)

^b UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Artificial neural network; Biosensor; Complex compost system; Hydroquinone; Nonlinear determination

Indexed keywords

ANALYTICAL METHOD; ANALYTICAL TOOL; AROMATIC POLLUTANTS; ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORK ANALYSIS; ARTIFICIAL NEURAL NETWORKS; BIODEGRADATION PROCESS; COMPLEX COMPOST SYSTEM; COMPLEX ENVIRONMENTS; COMPOSTING SYSTEMS; DETECTING SYSTEMS; DETECTION RANGE; DIRECT DETECTION; HUMAN LIVES; HYDROQUINONE; INTERMEDIATE PRODUCT; NONLINEAR DETERMINATION; NONLINEAR RANGES; NONLINEAR REGRESSION MODELS; NOVEL NEURAL NETWORK; PREDICTION MODEL; QUANTITATIVE STUDY; REAL SAMPLES; ROBUST METHODS; SIMULATION ACCURACY;

BACKPROPAGATION; BIOCHEMICAL ENGINEERING; BIODEGRADATION; COMPOSTING; ELECTRIC NETWORK ANALYSIS; KETONES; NEURAL NETWORKS; PHENOLS; REGRESSION ANALYSIS; SIGNAL TRANSDUCTION; UNCERTAINTY ANALYSIS;

BIOSENSORS;

HYDROQUINONE;

ANALYTIC METHOD; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOSENSOR; COMPOST; COMPOSTING; CONTROLLED STUDY; MEASUREMENT; NONLINEAR SYSTEM; PREDICTION; QUANTITATIVE STUDY; SIMULATION;

EID: 66149179244     PISSN: 10928758     EISSN: None     Source Type: Journal    
DOI: 10.1089/ees.2008.0235     Document Type: Article

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