Results 1 to 10 of about 519,710 (307)

Research on Adaptive Model Predictive Control Algorithm for Remotely Operated Vehicle

Kongzhi Yu Xinxi Jishu, 2023
Aiming at the motion state coupling and model non-linearity of remotely operated vehicle (ROV) caused by the asymmetric structural layout of ROV and changes of center of gravity and center of buoyancy, this paper proposes a model identification algorithm
ZHU Yinggu, ZHANG Dinghua, TU Shaoping, SONG Junhui   +3 more
doaj   +3 more sources

Upgrading Behavioral Models for the Design of Digital Predistorters

Sensors, 2021
This work presents a strategy to upgrade models for power amplifier (PA) behavioral modeling and digital predistortion (DPD). These incomplete structures are the consequence of nonlinear order and memory depth model truncation with the purpose of ...
Carlos Crespo-Cadenas, María José Madero-Ayora, Juan A. Becerra   +2 more
doaj   +1 more source

A Bivariate Volterra Series Model for the Design of Power Amplifier Digital Predistorters

Sensors, 2021
The operation of the power amplifier (PA) in wireless transmitters presents a trade-off between linearity and power efficiency, being more efficient when the device exhibits the highest nonlinearity.
Carlos Crespo-Cadenas, María J. Madero-Ayora, Juan A. Becerra   +2 more
doaj   +1 more source

Model parameter on-line identification with nonlinear parametrization – manipulator model

Technical Transactions, 2022
This paper presents an example of solving the parameter identification problem in the case of a robot with two degrees of freedom. In this study, a weighted recursive least squares algorithm was generalised to a case of nonlinear parameterisation in ...
Cedro Leszek
doaj   +1 more source

The identification method of the coal mill motor power model with the use of machine learning techniques [PDF]

Bulletin of the Polish Academy of Sciences: Technical Sciences, 1921
The article presents an identification method of the model of the ball-and-race coal mill motor power signal with the use of machine learning techniques. The stages of preparing training data for model parameters identification purposes are described, as
Zofia Magdalena Łabęda-Grudziak, Mariusz Lipiński   +1 more
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Design of extended Kalman filtering neural network control system based on particle swarm identification of nonlinear U-model

Automatika, 2022
This paper studies the modelling of a class of nonlinear plants with known structures but unknown parameters and proposes a general nonlinear U-model expression. The particle swarm optimization algorithm is used to identify the time-varying parameters of
Fengxia Xu, Xinyu Zhang, Zhongda Lu, Shanshan Wang   +3 more
doaj   +1 more source

Data-Driven Identification of Nonlinear Flame Models [PDF]

Journal of Engineering for Gas Turbines and Power, 2020
Abstract This paper presents a data-driven identification framework with the objective to retrieve a flame model from the nonlinear limit cycle. The motivation is to identify a flame model for configurations, which do not allow the determination of the flame dynamics: that is commonly for industrial applications where (i) optical access ...
Ghani, Abdulla, Boxx, Isaac, Noren, Carrie   +2 more
openaire   +2 more sources

A novel strategy for force identification of nonlinear structures

Journal of Low Frequency Noise, Vibration and Active Control, 2022
Dynamic force is the key indicator for monitoring the condition of a mechanical product. These mechanical structures always encompass some nonlinear factors. Most previous studies focused on obtaining the dynamic force of linear structures. Consequently,
Jie Liu, Tianqi Ding, Shanhui Liu, Bingbing Hu   +3 more
doaj   +1 more source

Clamp Nonlinear Modeling and Hysteresis Model Parameter Identification

IEEE Access, 2021
Based on the Bouc-Wen model, a nonlinear hysteretic restoring force model is established with dynamic equations. The hardening and softening of the material after reaching the yield limit are described by the nonlinear restoring force-displacement ...
Junzhe Lin, Zhihong Niu, Xufang Zhang, Hui Ma, Yulai Zhao, Qingkai Han   +5 more
doaj   +1 more source

Integrated Pre-Processing for Bayesian Nonlinear System Identification with Gaussian Processes [PDF]

, 2013
We introduce GP-FNARX: a new model for nonlinear system identification based on a nonlinear autoregressive exogenous model (NARX) with filtered regressors (F) where the nonlinear regression problem is tackled using sparse Gaussian processes (GP).
Frigola, Roger, Rasmussen, Carl Edward
core   +1 more source