Particle size estimate of grinding processes using random vector functional link networks with improved robustness

Neurocomputing

Volumn 169, Issue , 2015, Pages 361-372

  Dai, Wei ^a   Liu, Qiang ^a   Chai, Tianyou ^a  

^a STATE KEY LABORATORY OF SYNTHETICAL AUTOMATION FOR PROCESS INDUSTRIES   (China)

Author keywords

Grinding processes; Hybrid model; Particle size; Random vector functional link network; Robustness

Indexed keywords

GRINDING (MACHINING); LEAST SQUARES APPROXIMATIONS; POPULATION STATISTICS; ROBUSTNESS (CONTROL SYSTEMS); TIME VARYING NETWORKS;

COMPENSATION MODELING; FUNCTIONAL-LINK NETWORK; GRINDING PROCESS; HARD-WARE-IN-THE-LOOP; HYBRID MODEL; KERNEL DENSITY ESTIMATION; POPULATION BALANCES; WEIGHTED LEAST SQUARES;

PARTICLE SIZE;

ARTICLE; CALCULATION; CONTROLLED STUDY; GRINDING; INFORMATION PROCESSING; INFORMATION PROCESSING BIAS; KERNEL METHOD; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PARTICLE SIZE; PRIORITY JOURNAL; PROCESS OPTIMIZATION; RANDOM VECTOR FUNCTIONAL LINK NETWORK; REGRESSION ANALYSIS;

EID: 84938215386     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.08.098     Document Type: Article

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