An adaptive gradient-descent-based neural networks for the on-line solution of linear time variant equations and its applications

Cai, Jianfei; Chen, Yi

doi:10.1016/j.ins.2022.11.157

Cited by 9 publications

(3 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, g is the derivative of the activation function; if g > 1, the final derived gradient update increases exponentially as the number of layers increases, that is, a gradient explosion occurs, and if g < 1, then the derived gradient update information decays exponentially as the number of layers increases, that is, a gradient disappearance occurs [16].…”

Section: Theoretical Analysis Of Data Collapse Phenomenon Based On Gr...mentioning

confidence: 99%

A Microwave Radiometer Residual Inversion Neural Network Based on a Deadband Conditioning Model

Zhao,

Wu,

et al. 2023

JMSE

View full text Add to dashboard Cite

Microwave radiometers are passive remote sensing devices that are widely used in marine atmospheric observations. The accuracy of its inversion of temperature and humidity profiles is an important indicator of its performance. Back Propagation (BP) neural networks are widely used in the study of microwave radiometer inversion problems. However, the BP network which is carried by the radiometer inversion suffers from profile data collapse. To address this, this study introduced a residual network to improve the accuracy of water vapor vertical profiles. Aiming at the problem of large inversion temperature error due to the effect of turbulence on the light-travel phase induced by stationary fronts along the seashore in the subtropical monsoon climate region, we used historical data to establish the seasonal a priori mean profile and design a dead-zone residual adjustment model. The accuracy of the residual network and the deadband-adjusted residual network was verified using the meteorological records of the Taizhou region from 2013–2018, with the experimental data and BP hierarchical network as the comparison term. We found no data collapse in the temperature and humidity profile inversion results of the residual network. Relative to the initial BP hierarchical algorithm, where the error of water vapor in the range 6–10 km was reduced by 80%, the dead zone residual adjustment model in the inverse-temperature phenomenon reduced the sum of squares error by 21%, compared with the ordinary residual network inversion results. Our findings provide new insights into the accuracy improvement of radiometer remote sensing.

show abstract

Section: Theoretical Analysis Of Data Collapse Phenomenon Based On Gr...mentioning

confidence: 99%

A Microwave Radiometer Residual Inversion Neural Network Based on a Deadband Conditioning Model

Zhao,

Wu,

et al. 2023

JMSE

View full text Add to dashboard Cite

show abstract

“…Typically, due to the use of fixed sampling periods and fixed convergence factors in the conventional DRNN algorithms mentioned above, it is difficult for them to achieve a balance in computational precision and convergence rate, resulting in limited algorithmic dynamic and convergence performance. Therefore, some researchers have tried to introduce various adaptive mechanisms into model/algorithm design (Song et al, 2008 ; Yang M. et al, 2020 ; Dai et al, 2022 ; Cai and Yi, 2023 ). For example, Yang M. et al ( 2020 ) proposed two discretized RNN algorithms with an adaptive Jacobian matrix.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Yang M. et al ( 2020 ) proposed two discretized RNN algorithms with an adaptive Jacobian matrix. Cai and Yi ( 2023 ) developed an adaptive gradient-descent-based RNN model to solve time-variant problems based on the Lyapunov theory. Dai et al ( 2022 ) proposed a hybrid RNN model by introducing a fuzzy adaptive control strategy to generate a fuzzy adaptive factor that can change its size adaptively according to the RE.…”

Section: Introductionmentioning

confidence: 99%

An adaptive discretized RNN algorithm for posture collaboration motion control of constrained dual-arm robots

Zhang,

Han,

Qiu

2024

Front. Neurorobot.

View full text Add to dashboard Cite

Although there are many studies on repetitive motion control of robots, few schemes and algorithms involve posture collaboration motion control of constrained dual-arm robots in three-dimensional scenes, which can meet more complex work requirements. Therefore, this study establishes the minimum displacement repetitive motion control scheme for the left and right robotic arms separately. On the basis of this, the design mentality of the proposed dual-arm posture collaboration motion control (DAPCMC) scheme, which is combined with a new joint-limit conversion strategy, is described, and the scheme is transformed into a time-variant equation system (TVES) problem form subsequently. To address the TVES problem, a novel adaptive Taylor-type discretized recurrent neural network (ATT-DRNN) algorithm is devised, which fundamentally solves the problem of calculation accuracy which cannot be balanced well with the fast convergence speed. Then, stringent theoretical analysis confirms the dependability of the ATT-DRNN algorithm in terms of calculation precision and convergence rate. Finally, the effectiveness of the DAPCMC scheme and the excellent convergence competence of the ATT-DRNN algorithm is verified by a numerical simulation analysis and two control cases of dual-arm robots.

show abstract

Noise-tolerant zeroing neural network control for a novel compliant actuator in lower-limb exoskeletons

Sun,

Xu,

Wang

et al. 2024

Neural Comput & Applic

View full text Add to dashboard Cite

An adaptive gradient-descent-based neural networks for the on-line solution of linear time variant equations and its applications

Cited by 9 publications

References 32 publications

A Microwave Radiometer Residual Inversion Neural Network Based on a Deadband Conditioning Model

A Microwave Radiometer Residual Inversion Neural Network Based on a Deadband Conditioning Model

An adaptive discretized RNN algorithm for posture collaboration motion control of constrained dual-arm robots

Noise-tolerant zeroing neural network control for a novel compliant actuator in lower-limb exoskeletons

Contact Info

Product

Resources

About