Cardinality-constrained portfolio selection based on collaborative neurodynamic optimization
Portfolio optimization is one of the most important investment strategies in financial markets.
It is practically desirable for investors, especially high-frequency traders, to consider …
It is practically desirable for investors, especially high-frequency traders, to consider …
Finite-time synchronization of Markovian coupled neural networks with delays via intermittent quantized control: Linear programming approach
R Tang, H Su, Y Zou, X Yang - IEEE Transactions on Neural …, 2021 - ieeexplore.ieee.org
This article is devoted to investigating finite-time synchronization (FTS) for coupled neural
networks (CNNs) with time-varying delays and Markovian jumping topologies by using an …
networks (CNNs) with time-varying delays and Markovian jumping topologies by using an …
Sparse signal reconstruction via collaborative neurodynamic optimization
H Che, J Wang, A Cichocki - Neural Networks, 2022 - Elsevier
In this paper, we formulate a mixed-integer problem for sparse signal reconstruction and
reformulate it as a global optimization problem with a surrogate objective function subject to …
reformulate it as a global optimization problem with a surrogate objective function subject to …
A coordinated model for multiple electric vehicle aggregators to grid considering imbalanced liability trading
In the interaction between electric vehicle (EV) and grid, research on the coordinated
optimization of different EV aggregators to reduce the adverse effect of EVs uncertainty is of …
optimization of different EV aggregators to reduce the adverse effect of EVs uncertainty is of …
Centralized and Collective Neurodynamic Optimization Approaches for Sparse Signal Reconstruction via L₁-Minimization
This article develops several centralized and collective neurodynamic approaches for
sparse signal reconstruction by solving the-minimization problem. First, two centralized …
sparse signal reconstruction by solving the-minimization problem. First, two centralized …
An inertial neural network approach for robust time-of-arrival localization considering clock asynchronization
C Xu, Q Liu - Neural Networks, 2022 - Elsevier
This paper presents an inertial neural network to solve the source localization optimization
problem with l 1-norm objective function based on the time of arrival (TOA) localization …
problem with l 1-norm objective function based on the time of arrival (TOA) localization …
Distributed continuous and discrete time projection neurodynamic approaches for sparse recovery
Sparse representation acts as a fundamental data science methodology for solving a wide
range of problems in machine learning and engineering. In this paper, we respectively …
range of problems in machine learning and engineering. In this paper, we respectively …
Smoothing inertial neurodynamic approach for sparse signal reconstruction via Lp-norm minimization
In this paper, we propose a smoothing inertial neurodynamic approach (SINA) which is used
to deal with L p-norm minimization problem to reconstruct sparse signals. Note that the …
to deal with L p-norm minimization problem to reconstruct sparse signals. Note that the …
Neurodynamic approaches for sparse recovery problem with linear inequality constraints
This paper develops two neurodynamic approaches for solving the L 1-minimization
problem with the linear inequality constraints. First, a centralized neurodynamic approach is …
problem with the linear inequality constraints. First, a centralized neurodynamic approach is …
A comparative study of multi-objective optimization algorithms for sparse signal reconstruction
ME Erkoc, N Karaboga - Artificial Intelligence Review, 2022 - Springer
The development of the efficient sparse signal recovery algorithm is one of the important
problems of the compressive sensing theory. There exist many types of sparse signal …
problems of the compressive sensing theory. There exist many types of sparse signal …