Prescribed-Time Convergent Adaptive ZNN for Time-Varying Matrix Inversion under Harmonic Noise

Liao, Bolin; Han, Luyang; He, Yongjun; Cao, Xinwei; Li, Jianfeng

doi:10.3390/electronics11101636

Cited by 13 publications

(7 citation statements)

References 40 publications

(97 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neural networks have been developed rapidly and have presented many new-type models in recent years [ 19 , 20 ]. They are capable of performing complex computational tasks and can also be applied to path planning.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Path Planning Based on Six-Direction Search Scheme

Li,

Tang

et al. 2024

Sensors

View full text Add to dashboard Cite

In order to solve the problem of how to perform path planning for AUVs with multiple obstacles in a 3D underwater environment, this paper proposes a six-direction search scheme based on neural networks. In known environments with stationary obstacles, the obstacle energy is constructed based on a neural network and the path energy is introduced to avoid a too-long path being generated. Based on the weighted total energy of obstacle energy and path energy, a six-direction search scheme is designed here for path planning. To improve the efficiency of the six-direction search algorithm, two optimization methods are employed to reduce the number of iterations and total path search time. The first method involves adjusting the search step length dynamically, which helps to decrease the number of iterations needed for path planning. The second method involves reducing the number of path nodes, which can not only decrease the search time but also avoid premature convergence. By implementing these optimization methods, the performance of the six-direction search algorithm is enhanced in favor of path planning with multiple underwater obstacles reasonably. The simulation results validate the effectiveness and efficiency of the six-direction search scheme.

show abstract

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Path Planning Based on Six-Direction Search Scheme

Li,

Tang

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Among these noises, linear noises are universal in various engineering applications, and the non‐linear noises can be converted into linear noises by Taylor's formula. Furthermore, many ZNNs with noise tolerance have been widely proposed and applied [31‐34]. For instance, Jin et al.…”

Section: Introductionmentioning

confidence: 99%

“…Among these noises, linear noises are universal in various engineering applications, and the non-linear noises can be converted into linear noises by Taylor's formula. Furthermore, many ZNNs with noise tolerance have been widely proposed and applied [31][32][33][34]. For instance, Jin et al proposed an integrated enhanced ZNN (integral-enhanced ZNN (IEZNN)) to suppress all kinds of noises to a certain extent [35].…”

Section: Introductionmentioning

confidence: 99%

Double integral‐enhanced Zeroing neural network with linear noise rejection for time‐varying matrix inverse

Liao

Han

Cao

et al. 2023

CAAI Trans on Intel Tech

Self Cite

View full text Add to dashboard Cite

In engineering fields, time‐varying matrix inversion (TVMI) issue is often encountered. Zeroing neural network (ZNN) has been extensively employed to resolve the TVMI problem. Nevertheless, the original ZNN (OZNN) and the integral‐enhanced ZNN (IEZNN) usually fail to deal with the TVMI problem under unbounded noises, such as linear noises. Therefore, a neural network model that can handle the TVMI under linear noise interference is urgently needed. This paper develops a double integral‐enhanced ZNN (DIEZNN) model based on a novel integral‐type design formula with inherent linear‐noise tolerance. Moreover, its convergence and robustness are verified by derivation strictly. For comparison and verification, the OZNN and the IEZNN models are adopted to resolve the TVMI under multiple identical noise environments. The experiments proved that the DIEZNN model has excellent advantages in solving TVMI problems under linear noises. In general, the DIEZNN model is an innovative work and is proposed for the first time. Satisfyingly, the errors of DIEZNN are always less than 1 × 10−3 under linear noises, whereas the error norms of OZNN and IEZNN models are not convergent to zero. In addition, these models are applied to the control of the controllable permanent magnet synchronous motor chaotic system to indicate the superiority of the DIEZNN.

show abstract

“…At present, the research on more challenging time-varying problems has become a new hotspot, and many new methods have been proposed and applied [1][2][3][4][5]. As a neural dynamics method with neural network background, the zeroing neural dynamics (ZND) method is proposed and applied to solve different kinds of time-varying problems [6][7][8][9][10][11][12][13][14][15][16], such as time-varying linear matrix inequality [6], robot control [9], corona virus disease diagnosis [10], matrix inversion [13,14], and timevarying nonlinear optimization [16]. Generally, the problem solving model obtained by using the ZND method is a continuous one.…”

Section: Introductionmentioning

confidence: 99%

Continuous and Discrete ZND Models with Aid of Eleven Instants for Complex QR Decomposition of Time-Varying Matrices

Chen,

Kang,

Zhang

2023

Mathematics

View full text Add to dashboard Cite

The problem of QR decomposition is considered one of the fundamental problems commonly encountered in both scientific research and engineering applications. In this paper, the QR decomposition for complex-valued time-varying matrices is analyzed and investigated. Specifically, by applying the zeroing neural dynamics (ZND) method, dimensional reduction method, equivalent transformations, Kronecker product, and vectorization techniques, a new continuous-time QR decomposition (CTQRD) model is derived and presented. Then, a novel eleven-instant Zhang et al discretization (ZeaD) formula, with fifth-order precision, is proposed and studied. Additionally, five discrete-time QR decomposition (DTQRD) models are further obtained by using the eleven-instant and other ZeaD formulas. Theoretical analysis and numerical experimental results confirmed the correctness and effectiveness of the proposed continuous and discrete ZND models.

show abstract

Prescribed-Time Convergent Adaptive ZNN for Time-Varying Matrix Inversion under Harmonic Noise

Cited by 13 publications

References 40 publications

Three-Dimensional Path Planning Based on Six-Direction Search Scheme

Three-Dimensional Path Planning Based on Six-Direction Search Scheme

Double integral‐enhanced Zeroing neural network with linear noise rejection for time‐varying matrix inverse

Continuous and Discrete ZND Models with Aid of Eleven Instants for Complex QR Decomposition of Time-Varying Matrices

Contact Info

Product

Resources

About