Review of soft sensor methods for regression applications

FAA Souza, R Araújo, J Mendes - Chemometrics and Intelligent Laboratory …, 2016‏ - Elsevier
Soft sensors for regression applications (SSR) are inferential models that use online
available sensors (eg temperature, pressure, flow rate, etc.) to predict quality variables …

[HTML][HTML] Input selection methods for soft sensor design: A survey

F Curreri, G Fiumara, MG **bilia - Future Internet, 2020‏ - mdpi.com
Soft Sensors (SSs) are inferential models used in many industrial fields. They allow for real-
time estimation of hard-to-measure variables as a function of available data obtained from …

Nonlinear black-box system identification through coevolutionary algorithms and radial basis function artificial neural networks

HVH Ayala, D Habineza, M Rakotondrabe… - Applied Soft …, 2020‏ - Elsevier
The present work deals with the application of coevolutionary algorithms and artificial neural
networks to perform input selection and related parameter estimation for nonlinear black-box …

Adaptive soft sensor modeling framework based on just-in-time learning and kernel partial least squares regression for nonlinear multiphase batch processes

H **, X Chen, J Yang, L Wu - Computers & Chemical Engineering, 2014‏ - Elsevier
Batch processes are characterized by inherent nonlinearity, multiple phases and time-
varying behavior that pose great challenges for accurate state estimation. A multiphase just …

GP-COACH: Genetic Programming-based learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems

FJ Berlanga, AJ Rivera, MJ del Jesús, F Herrera - Information Sciences, 2010‏ - Elsevier
In this paper we propose GP-COACH, a Genetic Programming-based method for the
learning of COmpact and ACcurate fuzzy rule-based classification systems for High …

Development and comparison of neural network based soft sensors for online estimation of cement clinker quality

AK Pani, VK Vadlamudi, HK Mohanta - ISA transactions, 2013‏ - Elsevier
The online estimation of process outputs mostly related to quality, as opposed to their
belated measurement by means of hardware measuring devices and laboratory analysis …

[HTML][HTML] Online identification of Takagi–Sugeno fuzzy models based on self-adaptive hierarchical particle swarm optimization algorithm

S Rastegar, R Araujo, J Mendes - Applied mathematical modelling, 2017‏ - Elsevier
This paper presents an approach for online learning of Takagi–Sugeno (TS) fuzzy models. A
novel learning algorithm based on a Hierarchical Particle Swarm Optimization (HPSO) is …

Hierarchical adaptive genetic algorithm based T–S fuzzy controller for non-linear automotive applications

EM Abdelrahim - International Journal of Fuzzy Systems, 2022‏ - Springer
In this paper, a robust and enhanced evolutionary computing assisted Takagi Sugeno (T–S)
fuzzy controller was developed for automotive fuel injection control. To augment the rule …

Vector optimization of laser solid freeform fabrication system using a hierarchical mutable smart bee-fuzzy inference system and hybrid NSGA-II/self-organizing map

A Fathi, A Mozaffari - Journal of Intelligent Manufacturing, 2014‏ - Springer
The purpose of current investigation is to develop a robust intelligent framework to achieve
efficient and reliable operating process parameters for laser solid freeform fabrication (LSFF) …

Genetic fuzzy system for data-driven soft sensors design

J Mendes, F Souza, R Araújo, N Gonçalves - Applied Soft Computing, 2012‏ - Elsevier
This paper proposes a new method for soft sensors (SS) design for industrial applications
based on a Takagi–Sugeno (T–S) fuzzy model. The learning of the T–S model is performed …