Results 81 to 90 of about 128,037 (313)

An Improved Method for Stochastic Nonlinear System’s Identification Using Fuzzy-Type Output-Error Autoregressive Hammerstein–Wiener Model Based on Gradient Algorithm, Multi-Innovation, and Data Filtering Techniques

Complexity, 2021
This paper proposes an innovative identification approach of nonlinear stochastic systems using Hammerstein–Wiener (HW) model with output-error autoregressive (OEA) noise.
Donia Ben Halima Abid, Saif Eddine Abouda, Hanane Medhaffar, Mohamed Chtourou   +3 more
doaj   +1 more source

A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems

International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.
A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam, Anthony Siming Chen, Guido Herrmann   +2 more
wiley   +1 more source

Identification of multi-model LPV models with two scheduling variables

, 2012
In order to model complex industrial processes, this work studies the identification of linear parameter varying (LPV) models with two scheduling variables.
吉国力, Bosch, PPJ Paul van den, Bosch, van den, P.P.J., Zhu, Yucai, Huang, Jiangyin, van den Bosch, Paul, Zhu, YC Youcai, Ji, G, Huang, J, Ji, G., Ji, Guoli, Zhu, Y., Huang, J.   +12 more
core   +1 more source

Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation. [PDF]

, 2008
In this paper, a continuous time recurrent neural network (CTRNN) is developed to be used in nonlinear model predictive control (NMPC) context. The neural network represented in a general nonlinear state-space form is used to predict the future ...
Cao, Yi, Al Seyab, Rihab Khalid Shakir
core   +1 more source

Nonlinear system identification using modified variational autoencoders

Intelligent Systems with Applications
This research proposes a methodology for identifying nonlinear systems using input/output data and deep learning generative models. Our framework integrates Variational Autoencoders (VAE) with Nonlinear Autoregressive with exogenous input (NARX) in a ...
Jose L. Paniagua, Jesús A. López
doaj   +1 more source

Identification of nonlinear-nonlinear neuron models and stimulus decoding [PDF]

BMC Neuroscience, 2013
The majority of neural encoding models employed today consist of a linear feature-selection stage followed by a static memoryless nonlinearity generating the neuronal rate of response. Although such linear-nonlinear (LN) models have been proven useful in characterizing computation in many neurons (see [1] and references therein), they exclude the ...
Lazar, Aurel A, Slutskiy, Yevgeniy B
openaire   +1 more source

Adaptive Observer for Coupled Wave PDE and Infinite ODE With Sampled Data and Unknown Input: Application to Brain Hemodynamics Estimation

International Journal of Adaptive Control and Signal Processing, EarlyView.
This article proposes a convergent adaptive observer for a damped wave PDE and an infinite‐dimensional ODE coupled in cascade using sampled‐in‐space ODE state measurements. The proposed observer estimates the distributed states of the PDE and ODE along with unknown PDE parameters and spatial input.
Zehor Belkhatir, Sarah Mechhoud, Taous‐Meriem Laleg‐Kirati   +2 more
wiley   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Nonlinear systems identification with automatic state/model size reduction using RESNETs and Group Lasso

, 2023
In the development of Model Predictive Control (MPC) systems, having a data-driven approach to automatically identify black-box dynamical models that are both accurate and computationally efficient is of paramount importance. In this work, we propose
L. Frascati, A. Bemporad
core  

System identification in production ecology: from theory to agroforestry practice [PDF]

, 2009
This paper introduces a system identification approach to agricultural ecosystems. In particular, the identification of an agroforestry system, combining trees with crops, is subject of study.
Graves, Anil R., Keesman, K.J., Graves, A., Karel J. Keesman, Anil Graves, van der Werf, Wopke, Keesman, K. J., Wopke van der Werf, Werf, W., van der, Burgess, Paul J., Burgess, P., Paul Burgess, van der Werf, W.   +12 more
core   +1 more source