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Review of soft sensor methods for regression applications
Soft sensors for regression applications (SSR) are inferential models that use online
available sensors (eg temperature, pressure, flow rate, etc.) to predict quality variables …
available sensors (eg temperature, pressure, flow rate, etc.) to predict quality variables …
[HTML][HTML] Input selection methods for soft sensor design: A survey
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 …
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
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 …
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
Batch processes are characterized by inherent nonlinearity, multiple phases and time-
varying behavior that pose great challenges for accurate state estimation. A multiphase just …
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
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 …
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
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 …
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
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 …
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
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 …
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
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) …
efficient and reliable operating process parameters for laser solid freeform fabrication (LSFF) …
Genetic fuzzy system for data-driven soft sensors design
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 …
based on a Takagi–Sugeno (T–S) fuzzy model. The learning of the T–S model is performed …