RNN models for dynamic matrix inversion: A control-theoretical perspective
In this paper, the existing recurrent neural network (RNN) models for solving zero-finding
(eg, matrix inversion) with time-varying parameters are revisited from the perspective of …
(eg, matrix inversion) with time-varying parameters are revisited from the perspective of …
[HTML][HTML] Short-term load and price forecasting using artificial neural network with enhanced Markov chain for ISO New England
A Alhendi, AS Al-Sumaiti, M Marzband, R Kumar… - Energy Reports, 2023 - Elsevier
Nowadays, forecasting methods have gained significant attention, particularly with the
design and development of energy systems. In fact, accurate load and price forecasting is …
design and development of energy systems. In fact, accurate load and price forecasting is …
A recurrent neural network with explicitly definable convergence time for solving time-variant linear matrix equations
W Li - IEEE Transactions on Industrial Informatics, 2018 - ieeexplore.ieee.org
Time-variant linear matrix equations (TVLMEs) are ubiquitous in engineering. To solve
TVLMEs, various zeroing neural network (ZNN) models have been developed. These ZNNs …
TVLMEs, various zeroing neural network (ZNN) models have been developed. These ZNNs …
Spatial task scheduling for cost minimization in distributed green cloud data centers
The infrastructure resources in distributed green cloud data centers (DGCDCs) are shared
by multiple heterogeneous applications to provide flexible services to global users in a high …
by multiple heterogeneous applications to provide flexible services to global users in a high …
Enhancing photovoltaic system modeling and control under partial and complex shading conditions using a robust hybrid DE-FFNN MPPT strategy
N Ncir, N El Akchioui, A El Fathi - Renewable Energy Focus, 2023 - Elsevier
Improving efficiency, reliability, and extending the life of Photovoltaic (PV) systems are some
essential benefits of developing an effective Maximum Power Point Tracking (MPPT) control …
essential benefits of developing an effective Maximum Power Point Tracking (MPPT) control …
Development of an adversarial transfer learning based soft sensor in industrial systems
Data-driven soft sensors are usually used to predict quality-related but hard-to-measure
variables in industrial systems. The acceptable prediction performance, however, mainly …
variables in industrial systems. The acceptable prediction performance, however, mainly …
Novel PI controller and ANN controllers-Based passive cell balancing for battery management system
The cycle life and efficiency of a battery pack get enhanced by employing an intelligent
supporting system with it called the Battery Management System (BMS). A novel …
supporting system with it called the Battery Management System (BMS). A novel …
Design and analysis of a hybrid GNN-ZNN model with a fuzzy adaptive factor for matrix inversion
J Dai, Y Chen, L Xiao, L Jia, Y He - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Motivated from the convergence capability achieved by gradient neural network (GNN) and
zeroing neural network (ZNN) for matrix inversion, in this article, a novel hybrid GNN-ZNN (H …
zeroing neural network (ZNN) for matrix inversion, in this article, a novel hybrid GNN-ZNN (H …
Integration of on-line control in optimal design of multimode power-split hybrid electric vehicle powertrains
The multimode power-split architecture for hybrid electric vehicle (HEV) powertrains is
generally known for the complexity of its operation. This paper first addresses the challenge …
generally known for the complexity of its operation. This paper first addresses the challenge …
Robust networking: Dynamic topology evolution learning for Internet of Things
The Internet of Things (IoT) has been extensively deployed in smart cities. However, with the
expanding scale of networking, the failure of some nodes in the network severely affects the …
expanding scale of networking, the failure of some nodes in the network severely affects the …