Accelerating a recurrent neural network to finite-time convergence for solving time-varying Sylvester equation by using a sign-bi-power activation function
Bartels–Stewart algorithm is an effective and widely used method with an O (n 3) time
complexity for solving a static Sylvester equation. When applied to time-varying Sylvester …
complexity for solving a static Sylvester equation. When applied to time-varying Sylvester …
Deep causal learning for robotic intelligence
Y Li - Frontiers in Neurorobotics, 2023 - frontiersin.org
This invited Review discusses causal learning in the context of robotic intelligence. The
Review introduces the psychological findings on causal learning in human cognition, as well …
Review introduces the psychological findings on causal learning in human cognition, as well …
Finite-time stability and its application for solving time-varying Sylvester equation by recurrent neural network
Y Shen, P Miao, Y Huang, Y Shen - Neural Processing Letters, 2015 - Springer
This paper investigates finite-time stability and its application for solving time-varying
Sylvester equation by recurrent neural network. Firstly, a new finite-time stability criterion is …
Sylvester equation by recurrent neural network. Firstly, a new finite-time stability criterion is …
A real-time sequential ship roll prediction scheme based on adaptive sliding data window
A ship roll prediction scheme is proposed using an adaptive sliding data window (SDW),
which is designed to represent time-varying nonlinear dynamics of ship roll motion. The …
which is designed to represent time-varying nonlinear dynamics of ship roll motion. The …
A learning-based model predictive control scheme and its application in biped locomotion
This paper proposes a learning-based model predictive control scheme. This scheme
divides the predictive model into a known nominal model and an unknown model residual …
divides the predictive model into a known nominal model and an unknown model residual …
Finite time dual neural networks with a tunable activation function for solving quadratic programming problems and its application
In this paper, finite time dual neural networks with a new activation function are presented to
solve quadratic programming problems. The activation function has two tunable parameters …
solve quadratic programming problems. The activation function has two tunable parameters …
Tracking control of modified Lorenz nonlinear system using ZG neural dynamics with additive input or mixed inputs
The tracking-control problem of a special nonlinear system (ie, the extension of a modified
Lorenz chaotic system) with additive input or the mixture of additive and multiplicative inputs …
Lorenz chaotic system) with additive input or the mixture of additive and multiplicative inputs …
A class of PSO-tuned controllers in Lorenz chaotic system
This paper considers the optimal robust control of chaotic systems subject to parameter
perturbations and measurement noise. Three novel dynamic tracking controllers are …
perturbations and measurement noise. Three novel dynamic tracking controllers are …
Higher-order analysis of kinematic singularities of lower pair linkages and serial manipulators
A Müller - Journal of Mechanisms and Robotics, 2018 - asmedigitalcollection.asme.org
Kinematic singularities of linkages are configurations where the differential mobility
changes. Constraint singularities are critical points of the constraint mapping defining the …
changes. Constraint singularities are critical points of the constraint mapping defining the …
[图书][B] Kinematic control of redundant robot arms using neural networks
Presents pioneering and comprehensive work on engaging movement in robotic arms, with
a specific focus on neural networks This book presents and investigates different methods …
a specific focus on neural networks This book presents and investigates different methods …