Comparison of universal approximators incorporating partial monotonicity by structure

Neural Networks

Volumn 23, Issue 4, 2010, Pages 471-475

  Minin, Alexey ^a   Velikova, Marina ^b   Lang, Bernhard ^a   Daniels, Hennie ^c,d  

^a OOO Siemens   (Russian Federation)

^b RADBOUD UNIVERSITY MEDICAL CENTER   (Netherlands)

^c TILBURG UNIVERSITY   (Netherlands)

^d ERASMUS UNIVERSITY ROTTERDAM   (Netherlands)

Author keywords

Model predictive control; Neural networks; Partial monotonicity; Reliable control; Robust modelling

Indexed keywords

APPROXIMATION ERRORS; APPROXIMATION PERFORMANCE; BEST FUNCTION; CONTROL LAWS; DATA SETS; IN-CONTROL; INPUT-OUTPUT RELATIONS; MIN-MAX; MONOTONE FUNCTIONS; MONOTONICITY; MULTI LAYER PERCEPTRON; RELIABLE CONTROL; SAFETY-CRITICAL DOMAIN; UNIVERSAL APPROXIMATION; UNIVERSAL APPROXIMATORS;

MODEL PREDICTIVE CONTROL; PREDICTIVE CONTROL SYSTEMS;

NEURAL NETWORKS;

ETHANE; ETHYLENE;

ACCURACY; ANALYTICAL EQUIPMENT; ANALYTICAL ERROR; ARTICLE; ARTIFICIAL NEURAL NETWORK; DATA BASE; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; MATHEMATICAL PARAMETERS; PREDICTION; PRIORITY JOURNAL; PROCESS OPTIMIZATION; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED;

EID: 77950297986     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2009.09.002     Document Type: Article

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