Application of support vector regression for developing soft sensors for nonlinear processes

Canadian Journal of Chemical Engineering

Volumn 88, Issue 5, 2010, Pages 696-709

  Chitralekha, Saneej B ^a   Shah, Sirish L ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Polymer extrusion; Soft sensor; Support vector regression; System identification

Indexed keywords

APPROXIMATION CAPABILITIES; ARX MODEL; BASIC IDEA; BLACK-BOX MODEL; COMPUTATIONAL TOOLS; COST-EFFICIENT; ETHYLENE VINYL ACETATES; FEATURE SPACE; GENERALISATION; HARDWARE SENSORS; HEURISTIC PROCEDURES; HIGH-DIMENSIONAL; IN-BUILDINGS; INDUSTRIAL CASE STUDY; INDUSTRIAL SCALE; INPUT VARIABLES; INPUT-OUTPUT DATA; LABORATORY SCALE; LEAST SQUARES; LOCAL MINIMUMS; MELT INDEX; NEUTRALISATION; NON-LINEAR MODEL; NONLINEAR KERNELS; NONLINEAR PROBLEMS; NONLINEAR PROCESS; OPTIMAL DELAY; OPTIMIZATION FORMULATIONS; OUTPUT VARIABLES; POLYMER EXTRUSION; PREDICTION ERRORS; RADIAL BASIS FUNCTIONS; SOFT SENSORS; SUPPORT VECTOR REGRESSION; SUPPORT VECTOR REGRESSIONS; SYSTEM IDENTIFICATIONS; TWIN SCREW;

COMPUTATIONAL METHODS; COMPUTER SIMULATION; CONVEX OPTIMIZATION; ETHYLENE; EXTRUSION; EXTRUSION DIES; HEURISTIC METHODS; HIGH DENSITY POLYETHYLENES; INDUSTRIAL APPLICATIONS; LINEAR TRANSFORMATIONS; OPTIMIZATION; POLYMERS; POLYVINYL ACETATES; RADIAL BASIS FUNCTION NETWORKS; REGRESSION ANALYSIS; SENSORS; VECTORS;

MATHEMATICAL MODELS;

EID: 79951983943     PISSN: 00084034     EISSN: 1939019X     Source Type: Journal    
DOI: 10.1002/cjce.20363     Document Type: Article

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