Defects monitoring of laser metal deposition using acoustic emission sensor
Laser metal deposition (LMD) is an advanced additive manufacturing (AM) process used to
build or repair metal parts layer by layer for a range of different applications. Any presence of …
build or repair metal parts layer by layer for a range of different applications. Any presence of …
Passivity analysis of coupled reaction-diffusion neural networks with Dirichlet boundary conditions
Two coupled reaction-diffusion neural networks (CRDNNs) with different dimensions of input
and output are considered in this paper. The only difference between them is whether time …
and output are considered in this paper. The only difference between them is whether time …
Defect classification of laser metal deposition using logistic regression and artificial neural networks for pattern recognition
Detecting laser metal deposition (LMD) defects is a key element of evaluating the probability
of failure of the produced part. Acoustic emission (AE) is an effective technique in LMD …
of failure of the produced part. Acoustic emission (AE) is an effective technique in LMD …
Data-driven charging strategy of PEVs under transformer aging risk
Big data analytics and plug-in electric vehicle (PEV) are the important elements of smart
grids in the future. This paper introduces a data-driven charging strategy for PEV-based …
grids in the future. This paper introduces a data-driven charging strategy for PEV-based …
A novel learning algorithm based on computing the rules' desired outputs of a TSK fuzzy neural network with non-separable fuzzy rules
A Salimi-Badr, MM Ebadzadeh - Neurocomputing, 2022 - Elsevier
In this paper, a novel learning approach to train fuzzy neural networks' parameters based on
calculating the desired outputs of their rules, is proposed. We describe the desired outputs of …
calculating the desired outputs of their rules, is proposed. We describe the desired outputs of …
IC-FNN: a novel fuzzy neural network with interpretable, intuitive, and correlated-contours fuzzy rules for function approximation
MM Ebadzadeh, A Salimi-Badr - IEEE Transactions on Fuzzy …, 2017 - ieeexplore.ieee.org
In this paper, a novel fuzzy neural network with intuitive, interpretable, and correlated-
contours fuzzy rules (IC-FNN), for function approximation, is presented. The surfaces of …
contours fuzzy rules (IC-FNN), for function approximation, is presented. The surfaces of …
A novel self-organizing fuzzy neural network to learn and mimic habitual sequential tasks
A Salimi-Badr, MM Ebadzadeh - IEEE transactions on …, 2020 - ieeexplore.ieee.org
In this article, a new self-organizing fuzzy neural network (FNN) model is presented which is
able to simultaneously and accurately learn and reproduce different sequences. Multiple …
able to simultaneously and accurately learn and reproduce different sequences. Multiple …
Adaptive Nonstationary Fuzzy Neural Network
Fuzzy neural network (FNN) plays an important role as an inference system in practical
applications. To enhance its ability of handling uncertainty without invoking high …
applications. To enhance its ability of handling uncertainty without invoking high …
Exponential stability of discrete-time uncertain neural networks with multiple time-varying leakage delays
E Suntonsinsoungvon, S Udpin - Mathematics and Computers in Simulation, 2020 - Elsevier
In this paper, we investigate a new exponential stability criterion for uncertain discrete-time
neural networks with both multiple leakage time-varying delays and discrete time-varying …
neural networks with both multiple leakage time-varying delays and discrete time-varying …
Robust extended dissipativity criteria for discrete-time uncertain neural networks with time-varying delays
In this draft, we consider the problem of robust extended dissipativity for uncertain discrete-
time neural networks (DNNs) with time-varying delays. By constructing appropriate …
time neural networks (DNNs) with time-varying delays. By constructing appropriate …