Activation functions in deep learning: A comprehensive survey and benchmark
Neural networks have shown tremendous growth in recent years to solve numerous
problems. Various types of neural networks have been introduced to deal with different types …
problems. Various types of neural networks have been introduced to deal with different types …
Review on deep learning applications in frequency analysis and control of modern power system
The penetration of renewable energy resources (RES) generation and the interconnection of
regional power grids in wide area and large scale have led the modern power system to …
regional power grids in wide area and large scale have led the modern power system to …
Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine
Wind turbines play a crucial role in renewable energy generation systems and are frequently
exposed to challenging operational environments. Monitoring and diagnosing potential …
exposed to challenging operational environments. Monitoring and diagnosing potential …
An improved quantum-inspired differential evolution algorithm for deep belief network
W Deng, H Liu, J Xu, H Zhao… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Deep belief network (DBN) is one of the most representative deep learning models.
However, it has a disadvantage that the network structure and parameters are basically …
However, it has a disadvantage that the network structure and parameters are basically …
Activation functions: Comparison of trends in practice and research for deep learning
Deep neural networks have been successfully used in diverse emerging domains to solve
real world complex problems with may more deep learning (DL) architectures, being …
real world complex problems with may more deep learning (DL) architectures, being …
fkan: Fractional kolmogorov-arnold networks with trainable jacobi basis functions
AA Aghaei - Neurocomputing, 2025 - Elsevier
Recent advancements in neural network design have given rise to the development of
Kolmogorov-Arnold Networks (KANs), which enhance interpretability and precision of these …
Kolmogorov-Arnold Networks (KANs), which enhance interpretability and precision of these …
Data-driven methods for predictive maintenance of industrial equipment: A survey
W Zhang, D Yang, H Wang - IEEE systems journal, 2019 - ieeexplore.ieee.org
With the tremendous revival of artificial intelligence, predictive maintenance (PdM) based on
data-driven methods has become the most effective solution to address smart manufacturing …
data-driven methods has become the most effective solution to address smart manufacturing …
Deep learning for prognostics and health management: State of the art, challenges, and opportunities
B Rezaeianjouybari, Y Shang - Measurement, 2020 - Elsevier
Improving the reliability of engineered systems is a crucial problem in many applications in
various engineering fields, such as aerospace, nuclear energy, and water declination …
various engineering fields, such as aerospace, nuclear energy, and water declination …
How important are activation functions in regression and classification? A survey, performance comparison, and future directions
AD Jagtap, GE Karniadakis - Journal of Machine Learning for …, 2023 - dl.begellhouse.com
Inspired by biological neurons, the activation functions play an essential part in the learning
process of any artificial neural network (ANN) commonly used in many real-world problems …
process of any artificial neural network (ANN) commonly used in many real-world problems …
Multiscale kernel based residual convolutional neural network for motor fault diagnosis under nonstationary conditions
Motor fault diagnosis is imperative to enhance the reliability and security of industrial
systems. However, since motors are often operated under nonstationary conditions, the high …
systems. However, since motors are often operated under nonstationary conditions, the high …