Lyapunov-based neural network model predictive control using metaheuristic optimization approach
This research introduces a new technique to control constrained nonlinear systems, named
Lyapunov-based neural network model predictive control using a metaheuristic optimization …
Lyapunov-based neural network model predictive control using a metaheuristic optimization …
Lazy-learning-based data-driven model-free adaptive predictive control for a class of discrete-time nonlinear systems
In this paper, a novel data-driven model-free adaptive predictive control method based on
lazy learning technique is proposed for a class of discrete-time single-input and single …
lazy learning technique is proposed for a class of discrete-time single-input and single …
Integrated soft sensor using just-in-time support vector regression and probabilistic analysis for quality prediction of multi-grade processes
Multi-grade processes have played an important role in the fine chemical and polymer
industries. An integrated nonlinear soft sensor modeling method is proposed for online …
industries. An integrated nonlinear soft sensor modeling method is proposed for online …
Survey of industrial optimized adaptive control
JM Martín‐Sánchez, JM Lemos… - International Journal of …, 2012 - Wiley Online Library
This survey paper aims, to present in a systematic way, the 'state‐of‐the‐art'of a class of so
called optimized adaptive control methodologies, where adaptive systems theory is …
called optimized adaptive control methodologies, where adaptive systems theory is …
[HTML][HTML] Input convex neural networks in nonlinear predictive control: A multi-model approach
M Ławryńczuk - Neurocomputing, 2022 - Elsevier
The presented input convex neural multi-modelling approach to Model Predictive Control
(MPC) has two essential advantages. Firstly, the MPC algorithm solves only convex …
(MPC) has two essential advantages. Firstly, the MPC algorithm solves only convex …
Stable adaptive PI control for permanent magnet synchronous motor drive based on improved JITL technique
In this paper, a stable adaptive PI control strategy based on the improved just-in-time
learning (IJITL) technique is proposed for permanent magnet synchronous motor (PMSM) …
learning (IJITL) technique is proposed for permanent magnet synchronous motor (PMSM) …
Fast just-in-time-learning recursive multi-output LSSVR for quality prediction and control of multivariable dynamic systems
Aiming at quality prediction and control of blast furnace (BF) ironmaking process
characterized by complicated nonlinear time-varying dynamics, this paper proposes a just-in …
characterized by complicated nonlinear time-varying dynamics, this paper proposes a just-in …
Ensemble locally weighted partial least squares as a just‐in‐time modeling method
H Kaneko, K Funatsu - AIChE Journal, 2016 - Wiley Online Library
The predictive ability of soft sensors, which estimate values of an objective variable y online,
decreases due to process changes in chemical plants. To reduce the decrease of predictive …
decreases due to process changes in chemical plants. To reduce the decrease of predictive …
Multi-view locally weighted regression for loss given default forecasting
Accurately forecasting loss given default (LGD) poses challenges, due to its highly skewed
distributions and complex nonlinear dependencies with predictors. To this end, we propose …
distributions and complex nonlinear dependencies with predictors. To this end, we propose …
Rethinking the value of just-in-time learning in the era of industrial big data
Just-in-time learning (JITL) has become a widely used industrial process modeling tool. With
the advent of the industrial big data era, rich data information has brought new opportunities …
the advent of the industrial big data era, rich data information has brought new opportunities …