graficas, tablasThis research develops a framework for the modeling and identification of hysteretic structural systems, which employs multilayer perceptrons and physical principles of structures. This framework consists of three hysteretic models and their training algorithms, and it is based on two models of the scientific machine learning field, called universal ordinary differential equations (UODEs) and physics-guided neural networks (PGNNs). The proposed hysteretic models are UODEs and correspond to equations of motion with system state dynamics, where multilayer perceptrons approximate the unknown components of the dynamics. The training of the models uses the theory of PGNNs and considers data and the physical principles of structur...
236 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.In this research, a new neura...
The identification of nonlinear dynamical systems is of great importance in many areas of engineeri...
236 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.In this research, a new neura...
This paper introduces explicit neural representations of fundamental hysteresis operators such as th...
This paper introduces explicit neural representations of fundamental hysteresis operators such as th...
This paper introduces explicit neural representations of fundamental hysteresis operators such as th...
This paper introduces explicit neural representations of fundamental hysteresis operators such as th...
This paper introduces explicit neural representations of fundamental hysteresis operators such as th...
This document presents the design ofneural models that can predict some parameters of afluid catalit...
A computationally efficient hysteresis model, based on a standalone deep neural network, with the ca...
A computationally efficient hysteresis model, based on a standalone deep neural network, with the ca...
Hysteretic system behavior is ubiquitous in science and engineering fields including measurement sys...
Abstract – Neural networks are used for identification and solving the coupled equations of motion o...
"A Neural Network (NN) approach for modelling dynamic hysteresis is presented. The modelling of the ...
A restoring-force model is a versatile mathematical model that can describe the relationship between...
236 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.In this research, a new neura...
The identification of nonlinear dynamical systems is of great importance in many areas of engineeri...
236 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.In this research, a new neura...
This paper introduces explicit neural representations of fundamental hysteresis operators such as th...
This paper introduces explicit neural representations of fundamental hysteresis operators such as th...
This paper introduces explicit neural representations of fundamental hysteresis operators such as th...
This paper introduces explicit neural representations of fundamental hysteresis operators such as th...
This paper introduces explicit neural representations of fundamental hysteresis operators such as th...
This document presents the design ofneural models that can predict some parameters of afluid catalit...
A computationally efficient hysteresis model, based on a standalone deep neural network, with the ca...
A computationally efficient hysteresis model, based on a standalone deep neural network, with the ca...
Hysteretic system behavior is ubiquitous in science and engineering fields including measurement sys...
Abstract – Neural networks are used for identification and solving the coupled equations of motion o...
"A Neural Network (NN) approach for modelling dynamic hysteresis is presented. The modelling of the ...
A restoring-force model is a versatile mathematical model that can describe the relationship between...
236 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.In this research, a new neura...
The identification of nonlinear dynamical systems is of great importance in many areas of engineeri...
236 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.In this research, a new neura...