[HTML][HTML] Artificial neural networks: a practical review of applications involving fractional calculus
E Viera-Martin, JF Gómez-Aguilar… - The European Physical …, 2022 - Springer
In this work, a bibliographic analysis on artificial neural networks (ANNs) using fractional
calculus (FC) theory has been developed to summarize the main features and applications …
calculus (FC) theory has been developed to summarize the main features and applications …
Artificial neural networks with conformable transfer function for improving the performance in thermal and environmental processes
JE Solís-Pérez, JA Hernández, A Parrales… - Neural Networks, 2022 - Elsevier
This research proposes a novel transfer function based on the hyperbolic tangent and the
Khalil conformable exponential function. The non-integer order transfer function offers a …
Khalil conformable exponential function. The non-integer order transfer function offers a …
Recent advances and applications of fractional-order neural networks
This paper focuses on the growth, development, and future of various forms of fractional-
order neural networks. Multiple advances in structure, learning algorithms, and methods …
order neural networks. Multiple advances in structure, learning algorithms, and methods …
Fractional rectified linear unit activation function and its variants
MS Job, PH Bhateja, M Gupta, K Bingi… - Mathematical …, 2022 - Wiley Online Library
This paper focuses on deriving and validating the fractional‐order form of rectified linear unit
activation function and its linear and nonlinear variants. The linear variants include the leaky …
activation function and its linear and nonlinear variants. The linear variants include the leaky …
Energy-efficient control of thermal comfort in multi-zone residential HVAC via reinforcement learning
ZK Ding, QM Fu, JP Chen, HJ Wu, Y Lu… - Connection Science, 2022 - Taylor & Francis
Energy efficient control of thermal comfort has been already an important part of residential
heating, ventilation, and air conditioning (HVAC) systems. However, the optimisation of …
heating, ventilation, and air conditioning (HVAC) systems. However, the optimisation of …
Physics-informed neural network algorithm for solving forward and inverse problems of variable-order space-fractional advection–diffusion equations
S Wang, H Zhang, X Jiang - Neurocomputing, 2023 - Elsevier
A new physics-informed neural network (PINN) algorithm is proposed to solve variable-order
space-fractional partial differential equations (PDEs). For the forward problem, PINN …
space-fractional partial differential equations (PDEs). For the forward problem, PINN …
State-of-charge estimation for Lithium-Ion batteries using Kalman filters based on fractional-order models
L Xing, L Ling, B Gong, M Zhang - Connection Science, 2022 - Taylor & Francis
The accuracy of state of charge estimation results will directly affect the performance of
battery management system. Due to such, we focus in this article on the SOC estimation of …
battery management system. Due to such, we focus in this article on the SOC estimation of …
Approximate solution to fractional order models using a new fractional analytical scheme
In the present work, a new fractional analytical scheme (NFAS) is developed to obtain the
approximate results of fourth-order parabolic fractional partial differential equations (FPDEs) …
approximate results of fourth-order parabolic fractional partial differential equations (FPDEs) …
Ritz approximate method for solving delay fractional optimal control problems
K Mamehrashi - Journal of Computational and Applied Mathematics, 2023 - Elsevier
In this paper, a numerical method based on the Müntz–Legendre polynomials for solving a
class of time-delay fractional optimal control problems (TDFOCPs) is presented. The concept …
class of time-delay fractional optimal control problems (TDFOCPs) is presented. The concept …
Hurst exponent estimation using neural network
S Mukherjee, B Sadhukhan, AK Das… - International Journal …, 2023 - inderscienceonline.com
The Hurst exponent is used to identify the autocorrelation structure of a stochastic time
series, which allows for detecting persistence in time series data. Traditional signal …
series, which allows for detecting persistence in time series data. Traditional signal …