Multi-kernel neural networks for nonlinear unsteady aerodynamic reduced-order modeling
This paper proposes the multi-kernel neural networks and applies them to model the
nonlinear unsteady aerodynamics at constant or varying flow conditions. Different from …
nonlinear unsteady aerodynamics at constant or varying flow conditions. Different from …
Novel physics-informed artificial neural network architectures for system and input identification of structural dynamics PDEs
Herein, two novel Physics Informed Neural Network (PINN) architectures are proposed for
output-only system identification and input estimation of dynamic systems. Using merely …
output-only system identification and input estimation of dynamic systems. Using merely …
Diagonal recurrent neural network based identification of nonlinear dynamical systems with Lyapunov stability based adaptive learning rates
This paper proposes a diagonal recurrent neural network (DRNN) based identification
model for approximating the unknown dynamics of the nonlinear plants. The proposed …
model for approximating the unknown dynamics of the nonlinear plants. The proposed …
Parametric system identification using neural networks
TA Tutunji - Applied Soft Computing, 2016 - Elsevier
Neural networks are used in many applications such as image recognition, classification,
control and system identification. However, the parameters of the identified system are …
control and system identification. However, the parameters of the identified system are …
Identification of nonlinear systems using adaptive variable-order fractional neural networks (Case study: A wind turbine with practical results)
Z Aslipour, A Yazdizadeh - Engineering Applications of Artificial …, 2019 - Elsevier
In this paper, a Variable-Order Fractional Single-layer Neural Network (VOFSNN) and a
Variable-Order Fractional Multi-layer Neural Network (VOFMNN) are proposed to identify …
Variable-Order Fractional Multi-layer Neural Network (VOFMNN) are proposed to identify …
Adaptive recurrent neural network with Lyapunov stability learning rules for robot dynamic terms identification
In this paper, a recurrent neural network coupled with Kalman filter is proposed to identify
dynamic terms of robotic manipulator. By cooperating some inherent characteristics of robot …
dynamic terms of robotic manipulator. By cooperating some inherent characteristics of robot …
Externally Recurrent Neural Network based identification of dynamic systems using Lyapunov stability analysis
This paper proposes an Externally Recurrent Neural Network (ERNN) for approximating the
unknown dynamics of complex nonlinear systems and time series prediction. The proposed …
unknown dynamics of complex nonlinear systems and time series prediction. The proposed …
Dynamic neural network-based feedback linearization control of full-car suspensions using PSO
This paper proposes a nonlinear control approach using dynamic neural network-based
input–output feedback linearization to resolve the inherent conflicting performance criteria …
input–output feedback linearization to resolve the inherent conflicting performance criteria …
Prediction and analysis of building energy efficiency using artificial neural network and design of experiments
Energy consumption of buildings is increasing steadily and occupying approximately 30-
40% of total energy use. It is important to predict heating and cooling loads of a building in …
40% of total energy use. It is important to predict heating and cooling loads of a building in …
Design of a novel robust recurrent neural network for the identification of complex nonlinear dynamical systems
A novel fully connected recurrent neural network (FCRNN) structure is proposed for the
identification of unknown dynamics of nonlinear systems. The proposed recurrent structure …
identification of unknown dynamics of nonlinear systems. The proposed recurrent structure …