Network (Bristol, England)
Oct 6, 2022
The features of memristive-coupled neural networks have been studied extensively in the continuous field. However, the particularities of the discrete domain are rarely mentioned. This paper constructs a discrete memristor with sine-type conductance ...
Network (Bristol, England)
Aug 4, 2022
Linear semidefinite programming problems have received a lot of attentions because of large variety of applications. This paper deals with a smooth gradient neural network scheme for solving semidefinite programming problems. According to some proper...
Network (Bristol, England)
Jul 12, 2022
The deep learning models play an essential role in many areas, including medical image analysis. These models extract important features without human intervention. In this paper, we propose a deep convolution neural network, named as deep U-Net mode...
Network (Bristol, England)
Apr 21, 2022
Nowadays, artificial intelligence has gained recognition in every aspect of life. Artificial neural networks, one of the most efficient artificial intelligence techniques, is remarkably successful in computers' acquisition of the learning and interpr...
Network (Bristol, England)
Apr 17, 2022
This paper presents a class of linear and nonlinear delay optimal control problems with mixed control-state constraints using a conformable fractional derivative. We modify the conformable fractional derivative using a novel translation from Caputo-F...
Network (Bristol, England)
Apr 5, 2022
This paper presents a framework for spiking neural networks to be prepared for the Integrated Information Theory (IIT) analysis, using a stochastic nonlinear integrate-and-fire model. The model includes the crucial dynamics of the all-or-none law and...
Network (Bristol, England)
Feb 23, 2022
The model adequacy and input significance tests have not been proposed as features for the specification of a single multiplicative neuron model artificial neural networks in the literature. Moreover, there is no systematic approach based on hypothes...
Network (Bristol, England)
Jan 9, 2022
In this paper, for the first time, the impact of the shape factor on the discharge coefficient of side orifices is evaluated using the novel Extreme Learning Machine (ELM) model. In addition, the Monte Carlo simulations (MCs) are applied to assess th...
Network (Bristol, England)
Jan 3, 2022
Neural networks have been extensively used for solving differential equations in the past, but they rely mostly on computationally expensive gradient-based numerical optimization procedure for solving differential equations. In this work, we are intr...
Network (Bristol, England)
Apr 19, 2021
A numerical method using neural network for solving infinite-horizon time-delayed optimal control problems is studied. The problem is first transformed, using a Páde approximation, to one without a time-delayed argument. By a suitable change of varia...