Performance comparison of artificial neural networks and expert systems applied to flow pattern identification in vertical ascendant gas-liquid flows

International Journal of Multiphase Flow

Volumn 36, Issue 9, 2010, Pages 738-754

  Rosa, E S ^a   Salgado, R M ^b   Ohishi, T ^a   Mastelari, N ^a  

^a UNIVERSITY OF CAMPINAS   (Brazil)

^b FEDERAL UNIVERSITY OF ALFENAS   (Brazil)

Author keywords

Clustering algorithms; Flow pattern recognition; Impedance sensor; Neural networks

Indexed keywords

AIR-WATER MIXTURE; ARTIFICIAL NEURAL NETWORK; ELECTRICAL RESISTIVITY; EXPERIMENTAL DATA; EXPERIMENTAL OBSERVATION; FLOW PATTERN IDENTIFICATION; FUZZY C MEAN; GAS LIQUID FLOWS; IMPEDANCE SENSOR; IMPEDANCE SENSORS; K-MEANS; KNOWLEDGE BASE; MULTIPLE LAYERS; MULTIPLE OUTPUTS; PERFORMANCE COMPARISON; PROBABILISTIC NEURAL NETWORKS; RADIAL BASIS FUNCTIONS; SELF-ORGANIZED MAPS; STATISTICAL MOMENTS; VISUAL INSPECTION;

CLUSTER ANALYSIS; ELECTRIC CONDUCTIVITY; EXPERT SYSTEMS; FLOW PATTERNS; LEARNING ALGORITHMS; NEURAL NETWORKS; PATTERN RECOGNITION SYSTEMS; PROBABILITY DENSITY FUNCTION; PROBLEM SOLVING; RADIAL BASIS FUNCTION NETWORKS; SENSORS; TWO PHASE FLOW; VISUAL COMMUNICATION; VOID FRACTION;

CLUSTERING ALGORITHMS;

EID: 77954761845     PISSN: 03019322     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijmultiphaseflow.2010.05.001     Document Type: Article

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