Gradient-based differential neural-solution to time-dependent nonlinear optimization
In this technical article, to seek the optimal solution to time-dependent nonlinear optimization
subject to linear inequality and equality constraints (TDNO-IEC), the gradient-based …
subject to linear inequality and equality constraints (TDNO-IEC), the gradient-based …
Distributed and time-delayed-winner-take-all network for competitive coordination of multiple robots
In this article, a distributed and time-delayed-winner-take-all (DT-WTA) network is
established and analyzed for competitively coordinated task assignment of a multirobot …
established and analyzed for competitively coordinated task assignment of a multirobot …
A strictly predefined-time convergent neural solution to equality-and inequality-constrained time-variant quadratic programming
Aiming at time-variant problems solving, a special type of recurrent neural networks, termed
zeroing neural network (ZNN), has been proposed, developed, and validated since 2001 …
zeroing neural network (ZNN), has been proposed, developed, and validated since 2001 …
Nonconvex activation noise-suppressing neural network for time-varying quadratic programming: Application to omnidirectional mobile manipulator
This article proposes an improved general zeroing neural network model to suppress noise
and to enhance the real-time performance of solving TVQP problems. The proposed model …
and to enhance the real-time performance of solving TVQP problems. The proposed model …
Robust k-WTA Network Generation, Analysis, and Applications to Multiagent Coordination
In this article, a robust-winner-take-all (-WTA) neural network employing the saturation-
allowed activation functions is designed and investigated to perform a-WTA operation, and …
allowed activation functions is designed and investigated to perform a-WTA operation, and …
RNN for perturbed manipulability optimization of manipulators based on a distributed scheme: A game-theoretic perspective
In order to leverage the unique advantages of redundant manipulators, avoiding the
singularity during motion planning and control should be considered as a fundamental issue …
singularity during motion planning and control should be considered as a fundamental issue …
Noise-suppressing neural dynamics for time-dependent constrained nonlinear optimization with applications
Up to date, the existing methods for nonlinear optimization with time-dependent parameters
can be classified into two types: 1) static methods are capable of handling inequality …
can be classified into two types: 1) static methods are capable of handling inequality …
An activated variable parameter gradient‐based neural network for time‐variant constrained quadratic programming and its applications
This study proposes a novel gradient‐based neural network model with an activated
variable parameter, named as the activated variable parameter gradient‐based neural …
variable parameter, named as the activated variable parameter gradient‐based neural …
A biologically inspired modified flower pollination algorithm for solving economic dispatch problems in modern power systems
Gradient-based traditional algorithms fail to locate optimal solutions for real-world problems
with non-differentiable/discontinuous objective functions. But biologically inspired …
with non-differentiable/discontinuous objective functions. But biologically inspired …
Recurrent neural dynamics models for perturbed nonstationary quadratic programs: A control-theoretical perspective
Recent decades have witnessed a trend that control-theoretical techniques are widely
leveraged in various areas, eg, design and analysis of computational models …
leveraged in various areas, eg, design and analysis of computational models …