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Article
Computer Science, Interdisciplinary Applications
Vasilios N. Katsikis et al.
Summary: The study focuses on the time-varying mean-variance portfolio selection (TV-MVPS) problem, using quadratic programming (QP) methods to address both static and time-varying scenarios. The TV-MVPS incorporates properties like moving averages and utilizes a linear-variational-inequality primal-dual neural network (LVI-PDNN) to offer an online solution. This innovative approach proves to be a robust alternative to conventional methods, providing real-time solutions to financial problems while overcoming static method limitations.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Automation & Control Systems
Jianhua Dai et al.
Summary: In this article, a novel fuzzy adaptive GNN-ZNN (FA-GNN-ZNN) model is proposed for matrix inversion. By introducing a fuzzy adaptive control strategy, the FA-GNN-ZNN model can adaptively change its size according to the residual error, leading to better performance compared to the existing H-GNN-ZNN model under the same conditions. Additionally, different activation functions are applied to the FA-GNN-ZNN model to further improve its performance, as supported by theoretical analysis and comparative simulation results.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Mathematics, Applied
Ashim Kumar et al.
Summary: This paper presents several explicit formulas for finding infinitely many solutions of the equation AXA = XAX when A is singular. It discusses techniques for determining projectors that commute with A and investigates the use of similarity transformations like Jordan and Schur decompositions to obtain new representations of the solutions. The paper also addresses the issue of computing solutions using finite precision arithmetic and proposes ideas for overcoming the difficulties that may arise. Numerical experiments provide insight into promising methods for solving the matrix equation numerically.
MEDITERRANEAN JOURNAL OF MATHEMATICS
(2022)
Article
Mathematics
Mariya Kornilova et al.
Summary: This study investigates the time-varying matrix pseudoinverse problem, proposes a new model based on SVD and ZNN methods, and validates the effectiveness of the model through numerical experiments.
Article
Computer Science, Information Systems
Theodore E. Simos et al.
Summary: This study proposes a family of higher-order ZNN (HOZNN) models, investigates their correlation with hyperpower iterations of arbitrary order, extends the original models to arbitrary time-dependent real matrices, and verifies their effectiveness through numerical testing and an application example.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Vasilios N. Katsikis et al.
Summary: The article investigates a complex-valued time-varying linear matrix equation problem and proposes a solution based on CVTVQR decomposition, which has shown effectiveness in numerical simulations and two applications.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Vasilios N. Katsikis et al.
Summary: The correlation between fuzzy logic systems (FLS) and zeroing neural networks (ZNN) design was investigated. It was shown that the gain parameter in ZNN design can be dynamically adjusted through a properly defined FLS. Dynamical systems for time-varying rank-deficient matrices were proposed and simulation experiments were performed to confirm the superiority of the FLS for dynamic adjustment of gain parameters.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Tian Shi et al.
Summary: This paper introduces a noise-tolerant zeroing neural network model (NTZNNM) for solving time-invariant/varying Yang-Baxter-type matrix equation problems efficiently with different measurement noises. Additionally, a general noise-tolerant zeroing neural network model (GNTZNNM) is developed to accelerate convergent rate and enhance robustness. The theoretical results demonstrate the global/exponential convergence ability of NTZNNM and investigate the global convergence of GNTZNDM with different activation functions in detail.
Article
Mechanics
R. S. Vieira et al.
Summary: The formal derivatives of the Yang-Baxter equation with respect to its spectral parameters yield two systems of differential equations, which can be simplified into two systems of polynomial equations by eliminating the derivatives of the R matrix elements. The polynomial systems have a non-zero Hilbert dimension and allow for solving some unknowns through simple differential equations, ensuring the uniqueness and generality of the solutions.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2021)
Article
Computer Science, Artificial Intelligence
Lei Jia et al.
Summary: This article introduces a FPZNN model to address the time-variant matrix Moore-Penrose inversion problem by incorporating a fuzzy power parameter generated from FLS. Analysis of the convergence and noise-tolerance of the FPZNN model confirms its superior performance, which is further demonstrated through simulative experiments under various activation functions.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Vasilios N. Katsikis et al.
Summary: In this paper, a continuous-time model is proposed for solving the time-varying problem of QR decomposition using the zeroing neural dynamics method, utilizing time derivative information from a known real or complex matrix. Theoretical analysis and numerical experiments demonstrate the effectiveness and convergence of the proposed method for solving the time-varying QR decomposition problem in both real and complex cases.
NEURAL PROCESSING LETTERS
(2021)
Article
Computer Science, Artificial Intelligence
Lei Jia et al.
Summary: A novel adaptive fuzzy-type zeroing neural network (AFT-ZNN) model is proposed in this article to solve time-variant quadratic programming problems by using an adaptive fuzzy control value to adjust its convergence rate. Results show that the AFT-ZNN model outperforms the traditional-type zeroing neural network (TT-ZNN) model in terms of performance.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Software Engineering
Predrag S. Stanimirovic et al.
OPTIMIZATION METHODS & SOFTWARE
(2020)
Article
Computer Science, Artificial Intelligence
Huamin Zhang et al.
Article
Computer Science, Artificial Intelligence
Predrag S. Stanimirovic et al.
Article
Mathematics, Applied
Haifeng Ma et al.
COMPUTATIONAL & APPLIED MATHEMATICS
(2019)
Article
Physics, Particles & Fields
Zengo Tsuboi
Article
Computer Science, Artificial Intelligence
Predrag S. Stanimirovic et al.
Article
Mathematics
Diogo Kendy Matsumoto et al.
TOKYO JOURNAL OF MATHEMATICS
(2015)
Article
Mathematics, Applied
Jiu Ding et al.
EAST ASIAN JOURNAL ON APPLIED MATHEMATICS
(2013)
Article
Computer Science, Artificial Intelligence
YN Zhang et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS
(2005)
Article
Statistics & Probability
G Khalaf et al.
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2005)
