AJUNTAMENT D'ALCOI
Website
Generalitat Valenciana
Website
Ayuntamiento de Valencia
Website
Cicloplast
Website
Ayuntamiento de Onil
Website
Anarpla
Website
Ayuntamiento de Mislata
Website
nlWA, North London Waste Authority
Website
Ayuntamiento de Salinas
Website
Zicla
Website
Fondazione Ecosistemi
Website
PEFC
Website
ALQUIENVAS
Website
DIPUTACI� DE VAL�NCIA
Website
AYUNTAMIENTO DE REQUENA
Website
UNIVERSIDAD DE ZARAGOZA
Website
OBSERVATORIO CONTRATACIÓN PÚBLICA
Website
AYUNTAMIENTO DE PAIPORTA
Website
AYUNTAMIENTO DE CUENCA
Website
BERL� S.A.
Website
CM PLASTIK
Website
TRANSFORMADORES INDUSTRIALES ECOL�GICOS
INDUSTRIAS AGAPITO
Website
RUBI KANGURO
Website
If you want to support our LIFE project as a STAKEHOLDER, please contact with us: life-future-project@aimplas.es
In this section, you can access to the latest technical information related to the FUTURE project topic.
Just-in-time learning based soft sensor with variable selection and weighting optimized by evolutionary optimization for quality prediction of nonlinear processes
Just-in-time (JIT) learning based soft sensors have been widely used for predicting product quality variables of nonlinear processes. They dynamically build online local models by selecting the samples most relevant to the query data from a historical database whenever an estimate is requested. However, building high-performance JIT soft sensors remains challenging due to difficulties defining similarity measures and the selection of input variables for facilitating efficient relevant sample selection and model building. In this study, we propose a novel soft sensing framework, referred to as JIT learning with variable selection and weighting (JIT-VSW). In this framework, a mixture weighted similarity (MWS) measure is defined by combining multiple weighted Euclidean distance (WED) based similarity measures. The MWS measure enables variable weighting embedded in WED measures to account for the relevance between input and output variables and facilitates the handling of highly complex process characteristics through the mixture-type similarity measure. Meanwhile, a wrapper optimization approach using evolutional algorithms is proposed for input variable selection. Further, the selection of input variables and the determination of MWS parameters, i.e., weights assigned to input variables and mixture coefficients of WED similarity measures, are formulated as a mixed integer optimization problem and solved simultaneously by using the mixed integer genetic algorithms (MIGA). The effectiveness and superiority of JIT-VSW are verified through three real-world applications.
» Author: Bei Pan, Huaiping Jin, Li Wang, Bin Qian, Xiangguang Chen, Si Huang, Jiangang Li
» Reference: 10.1016/j.cherd.2019.02.004
» Publication Date: 11/02/2019
C/ Gustave Eiffel, 4
(València Parc Tecnològic) - 46980
PATERNA (Valencia) - SPAIN
(+34) 96 136 60 40
Project Management department - Sustainability and Industrial Recovery
life-future-project@aimplas.es