Treffer: Estimation of effluent quality using PLS-based extreme learning machines : Extreme Learning Machines Theory & Applications

Title:
Estimation of effluent quality using PLS-based extreme learning machines : Extreme Learning Machines Theory & Applications
Authors:
LIJIE ZHAO, DIANHUI WANG, TIANYOU CHAI
Source:
Neural computing & applications (Print). 22(3-4):509-519
Publisher Information:
London: Springer, 2013.
Publication Year:
2013
Physical Description:
print, 16 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Neurology, Neurologie, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Analyse mathématique, Mathematical analysis, Calcul des variations et contrôle optimal, Calculus of variations and optimal control, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Applications, Fiabilité, test de durée de vie, contrôle de la qualité, Reliability, life testing, quality control, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Méthodes numériques en programmation mathématique, optimisation et calcul variationnel, Numerical methods in mathematical programming, optimization and calculus of variations, Optimisation et calcul variationnel numériques, Numerical methods in optimization and calculus of variations, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Calcul neuronal, Neural computation, computación neuronal, Entrée sortie, Input output, Entrada salida, Etude cas, Case study, Estudio caso, Fiabilité, Reliability, Fiabilidad, Implémentation, Implementation, Implementación, Loi conditionnelle, Conditional distribution, Ley condicional, Moindre carré partiel, Partial least squares, Minimos cuadrados parciales, Méthode optimisation, Optimization method, Método optimización, Optimisation, Optimization, Optimización, Projection orthogonale, Orthogonal projection, Proyección ortogonal, Qualité, Quality, Calidad, Régression PLS, PLS regression, Regresión PLS, Réseau neuronal, Neural network, Red neuronal, Traitement, Treatment, Tratamiento, 49XX, 62N05, 62P30, 65K10, 65Kxx, Apprentissage machine, Couche cachée, Extreme learning machine, Partial least square, Soft sensing, Wastewater treatment
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
The State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110004, Liaoning Province, China
The Key Laboratory of Chemical Industry Process Control Technology, Shenyang University of Chemical Technology, Shenyang 110042, Liaoning Province, China
Research Center of Automation, Northeastern University, Shenyang 110004, Liaoning Province, China
ISSN:
0941-0643
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=27659231
Rights:
Copyright 2014 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Mathematics
Accession Number:
edscal.27659231
Database:
PASCAL Archive

Weitere Informationen

The accurate and reliable measurement of effluent quality indices is essential for the implementation of successful control and optimization of wastewater treatment plants. In order to enhance the estimate performance in terms of accuracy and reliability, we present a partial least-squares-based extreme learning machine (called PLS-ELM) in this paper. The partial least squares (PLS) regression is applied to the ELM framework to improve the algebraic property of the hidden output matrix, which can be ill-conditional due to the high multicollinearity of the hidden layer output. The main idea behind our proposed PLS-ELM is to achieve a robust generalization performance by extracting a reduced number of latent variables from the hidden layer and using orthogonal projection operations. The results from a case study of a municipal wastewater treatment plant show that the PLS-ELM can effectively capture the input―output relationship with favorable performance against the conventional ELM.