Results 101 to 110 of about 16,686,076 (335)

Nonlinear System Identification Using Hammerstein-Wiener Neural Network and subspace algorithms [PDF]

Journal of Advances in Computer Engineering and Technology, 2015
Neural networks are applicable in identification systems from input-output data. In this report, we analyze theHammerstein-Wiener models and identify them.
Maryam Ashtari Mahini, Mohammad Teshnehlab, Mojtaba Ahmadieh khanehsar   +2 more
doaj  

Inverse Identification of Energy‐Dependent Laser Absorptivity in NiTi Laser Powder‐Bed Fusion via Calibrated Melt Pool Simulation

Advanced Engineering Materials, EarlyView.
A combined experimental–computational framework identifies energy‐dependent laser absorptivity for NiTi in laser powder‐bed fusion, applicable to conduction and transition modes. Single‐track experiments and thermofluid smoothed particle hydrodynamics simulations are coupled through inverse analysis of melt pool geometry.
Mohamadreza Afrasiabi, Hadi Moutablaleh, Ignatius A. Setiawan, Mehrshad Mehrpouya   +3 more
wiley   +1 more source

A Correlation Analysis-Based Hierarchical Identification Strategy for Hammerstein Models

Algorithms
Reliable mathematical models are essential for high-performance analysis and optimization of complex power and energy systems. However, inherent nonlinearities pose significant challenges to accurate model identification. The Hammerstein model, a typical
Qi Dong, Haolong Jiang, Qinyao Liu, Yuan Gao   +3 more
doaj   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

Advanced Engineering Materials, EarlyView.
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle, Cesar Alvarez‐Llamas, Vincent Motto‐Ros, Christophe Dujardin, Jérémie Margueritat, David Rodney   +5 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

Online Identification of Aircraft Model Parameters Based on Recursive Least Squares Method

Kongzhi Yu Xinxi Jishu, 2019
In order to realize online update of the flight control system, online assessment of flight capability and online detection of an aircraft fault, the online identification method of unknown parameters in the aircraft dynamics model was studied.
JIANG Yongming, WANG Changqing, XU Cheng
doaj  

New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design

Advanced Engineering Materials, EarlyView.
This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare, Y. Beideler, D. Chune, R. De Guio, T. Roland, H. Chibane   +5 more
wiley   +1 more source

IDENTIFICATION OF NONLINEAR MODEL PARAMETERS FOR RAPID THERMAL PROCESSES [PDF]

Научно-технический вестник информационных технологий, механики и оптики, 2014
A problem of parameters identification is considered for a nonlinear model of rapid thermal processes. A hybrid approach is proposed for parameter estimation combining both analytical solution and numerical optimization.
A. A. Kapitonov, S. V. Aranovskiy
doaj  

Bolt loosening detection in a jointed beam using empirical mode decomposition–based nonlinear system identification method

International Journal of Distributed Sensor Networks, 2019
In this work, a state-of-art nonlinear system identification method based on empirical mode decomposition is utilized and extended to detect bolt loosening in a jointed beam.
Chao Xu, Chen-Chen Huang, Wei-Dong Zhu
doaj   +1 more source