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pro vyhledávání: '"Hopfield neural networks"'
Autor:
Dehao Ruan, Yao Lu
Publikováno v:
AIMS Mathematics, Vol 9, Iss 8, Pp 22910-22926 (2024)
This paper centers on stochastic Hopfield neural networks with variable coefficients and infinite delay. First, we propose an integral inequality that improves and extends some existing works. Second, by employing some inequalities and stochastic ana
Externí odkaz:
https://doaj.org/article/df1e85a7b6934b28a02e60ad6d5b8042
Autor:
Lili Guo, Wanhui Huang
Publikováno v:
Frontiers in Physics, Vol 12 (2024)
Markovian jump Hopfield NNs (MJHNNs) have received considerable attention due to their potential for application in various areas. This paper deals with the issue of state estimation concerning a category of MJHNNs with discrete and distributed delay
Externí odkaz:
https://doaj.org/article/63574d1ea9724b47b4ef0b00334508e6
Publikováno v:
AIMS Mathematics, Vol 9, Iss 4, Pp 9232-9266 (2024)
Within the swiftly evolving domain of neural networks, the discrete Hopfield-SAT model, endowed with logical rules and the ability to achieve global minima of SAT problems, has emerged as a novel prototype for SAT solvers, capturing significant scien
Externí odkaz:
https://doaj.org/article/711dad9de18a4c5fbe57423d9ebf90cb
Autor:
Ravi P. Agarwal, Snezhana Hristova
Publikováno v:
AIMS Mathematics, Vol 8, Iss 11, Pp 26801-26820 (2023)
The general delay Hopfield neural network is studied. It is considered the case of time-varying delay, continuously distributed delays, time varying coefficients and a special type of a Riemann-Liouville fractional derivative (GPRLFD) with an exponen
Externí odkaz:
https://doaj.org/article/483bd3237ec44111af12c776b56483e5
Publikováno v:
Symmetry, Vol 16, Iss 6, p 740 (2024)
The purpose of this paper is to study the dynamics of Hopfield neural networks with impulsive effects, focusing on Poisson stable rates, synaptic connections, and unpredictable external inputs. Through the symmetry of impulsive and differential compa
Externí odkaz:
https://doaj.org/article/f7a422fd677a469585a3e306e7896eb8
Publikováno v:
Results in Control and Optimization, Vol 13, Iss , Pp 100329- (2023)
In this paper, split step theta balanced Euler approximations for stochastic time-varying delay Hopfield neural networks (HNN) with distributed delays are examined for their exponential stability and strong convergence. The primary goal of this study
Externí odkaz:
https://doaj.org/article/e11c36b2c65a4d11b1512f84c7bb7665
Publikováno v:
Frontiers in Neuroscience, Vol 17 (2023)
Alternative paradigms to the von Neumann computing scheme are currently arousing huge interest. Oscillatory neural networks (ONNs) using emerging phase-change materials like VO2 constitute an energy-efficient, massively parallel, brain-inspired, in-m
Externí odkaz:
https://doaj.org/article/0bd92376e55e4ea89ec10820a1050c52
Akademický článek
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Publikováno v:
IEEE Access, Vol 10, Pp 95369-95389 (2022)
The fully connected topology, which coordinates the connection of each neuron with all other neurons, remains the most commonly used structure in Hopfield-type neural networks. However, fully connected neurons may form a highly complex network, resul
Externí odkaz:
https://doaj.org/article/bba8191b118a4950ae00fd85cc5c72d5
Autor:
Bertrand Frederick Boui A Boya, Balamurali Ramakrishnan, Joseph Yves Effa, Jacques Kengne, Karthikeyan Rajagopal
Publikováno v:
Heliyon, Vol 9, Iss 2, Pp e13034- (2023)
This work studies the dynamics of a three dimensional Hopfield neural network focusing on the impact of bias terms. In the presence of bias terms, the models displays an odd symmetry and experiences typical behaviors including period doubling, sponta
Externí odkaz:
https://doaj.org/article/7a1cc643700e40a1aa75a1d57ef2e281