A Noise-Acceptable ZNN for Computing Complex-Valued Time-Dependent Matrix Pseudoinverse

Yue, Lei; Liao, Bolin; Yin, Qinye

doi:10.1109/access.2019.2894180

Cited by 21 publications

(5 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The corresponding property of FCZNN (9) and IEZNN (11) have been proven in [25] and [30], respectively. This section proves the global convergence of CVSZNN model (13) for solving complex-valued time-dependent matrix inverse.…”

Section: Theoretical Derivationsmentioning

confidence: 92%

See 1 more Smart Citation

Comprehensive Analysis of ZNN Models for Computing Complex-Valued Time-Dependent Matrix Inverse

2020

Self Cite

View full text Add to dashboard Cite

Computing complex-valued time-dependent matrix inverse is an important procedure in statistics and control theory. Zeroing neural network (ZNN) becomes an effective method for solving timedependent issues. This paper proposes a complex-valued synthetical ZNN (CVSZNN) model for computing complex-valued time-dependent matrix inverse within different noise environments. Two important property is synthetically dissected in the construction process of CVSZNN model. First, the finite-time convergence, to ensure the efficiency of CVSZNN model for solving complex-valued time-dependent issue. Secondly, the anti-noise property, to guarantee the robustness of CVSZNN model in presence of noises. Two important aspects above have been validated by theoretical analysis and numerical experiments. Furthermore, the impact of parameters values is investigated based on the experimental results. As compared with the existing ZNN models for computing complex-valued time-dependent matrix inverse, the superiority of the proposed CVSZNN model is displayed fully. INDEX TERMS Zeroing neural network, time-dependent, finite-time convergence, complex-valued, antinoise, matrix inverse.

show abstract

Section: Theoretical Derivationsmentioning

confidence: 92%

“…However, noise is ineluctable [29]. To overcome the drawback of (1), an integration-enhanced ZNN (IEZNN) is proposed in [30]. The design formula of IEZNN is provided asĖ…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Analysis of ZNN Models for Computing Complex-Valued Time-Dependent Matrix Inverse

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…As the authors conclude, the ICVRNN model has better performance for solving CVLEs compared to traditional neural network models. In addition, noise-tolerant complex-valued neural network models are widely used for solving many problems, such as matrix pseudo-inverse solving (Lei et al, 2019), robotics (Liao et al, 2022d), and non-linear optimization (Xiao et al, 2019a), etc.…”

Section: Noise-tolerancementioning

confidence: 99%

Advances on intelligent algorithms for scientific computing: an overview

et al. 2023

Self Cite

View full text Add to dashboard Cite

The field of computer science has undergone rapid expansion due to the increasing interest in improving system performance. This has resulted in the emergence of advanced techniques, such as neural networks, intelligent systems, optimization algorithms, and optimization strategies. These innovations have created novel opportunities and challenges in various domains. This paper presents a thorough examination of three intelligent methods: neural networks, intelligent systems, and optimization algorithms and strategies. It discusses the fundamental principles and techniques employed in these fields, as well as the recent advancements and future prospects. Additionally, this paper analyzes the advantages and limitations of these intelligent approaches. Ultimately, it serves as a comprehensive summary and overview of these critical and rapidly evolving fields, offering an informative guide for novices and researchers interested in these areas.

show abstract

“…Algorithms 2 and 5 are performed to solve the matrix inversion problem of (15) by using the 4-point ZeaD formula (14), and the fourth extrapolation formula in Table 2 is used in Algorithm 5. Additionally, the initial value X 0 is set to [0.5, −0.5; 0.5, 0.5].…”

Section: B Future Matrix Inversion Problemmentioning

confidence: 99%

New Models for Future Problems Solving by Using ZND Method, Correction Strategy and Extrapolation Formulas

Zhang

2019

IEEE Access

View full text Add to dashboard Cite

Time-variant problems, which can be classified into future and non-future problems, are often encountered in academia and industry. In a future problem, we only know the information on the current and past time instants, and we have to acquire the next-time-instant solution before the next time instant arrives. Zeroing neural dynamics (ZND) and Zhang et al. discretization (ZeaD) formula group are two essential tools to build discrete-time ZND (DT-ZND) models for future problems solving. The former uses a systematical design formula to build a continuous-time ZND (CT-ZND) model, and the latter is used to transform the CT-ZND model into the discrete-time forms. Many DT-ZND models have been developed to solve various time-variant problems. In light of DT-ZND models and correction strategy, in this paper, we mainly focus on designing and building improved models for future problems solving. Based on the ZND method, extrapolation formulas, and correction steps, new models and corresponding computational algorithms are proposed to solve future optimization and future matrix inversion problems. The numerical experiments are also carried out to demonstrate the superiority of the proposed algorithms.INDEX TERMS Future problem, zeroing neural dynamics (ZND), correction strategy, extrapolation formula.

show abstract

A Noise-Acceptable ZNN for Computing Complex-Valued Time-Dependent Matrix Pseudoinverse

Cited by 21 publications

References 32 publications

Comprehensive Analysis of ZNN Models for Computing Complex-Valued Time-Dependent Matrix Inverse

Comprehensive Analysis of ZNN Models for Computing Complex-Valued Time-Dependent Matrix Inverse

Advances on intelligent algorithms for scientific computing: an overview

New Models for Future Problems Solving by Using ZND Method, Correction Strategy and Extrapolation Formulas

Contact Info

Product

Resources

About