Time-varying convex optimization: Time-structured algorithms and applications
Optimization underpins many of the challenges that science and technology face on a daily
basis. Recent years have witnessed a major shift from traditional optimization paradigms …
basis. Recent years have witnessed a major shift from traditional optimization paradigms …
Dynamic neural network models for time-varying problem solving: a survey on model structures
C Hua, X Cao, Q Xu, B Liao, S Li - IEEE Access, 2023 - ieeexplore.ieee.org
In recent years, neural networks have become a common practice in academia for handling
complex problems. Numerous studies have indicated that complex problems can generally …
complex problems. Numerous studies have indicated that complex problems can generally …
Neural dynamics for cooperative control of redundant robot manipulators
In this paper, a neural-dynamic distributed scheme is proposed for the cooperative control of
multiple redundant manipulators with limited communications. It is guaranteed that, with the …
multiple redundant manipulators with limited communications. It is guaranteed that, with the …
RNN models for dynamic matrix inversion: A control-theoretical perspective
In this paper, the existing recurrent neural network (RNN) models for solving zero-finding
(eg, matrix inversion) with time-varying parameters are revisited from the perspective of …
(eg, matrix inversion) with time-varying parameters are revisited from the perspective of …
[HTML][HTML] Zeroing neural networks: A survey
Using neural networks to handle intractability problems and solve complex computation
equations is becoming common practices in academia and industry. It has been shown that …
equations is becoming common practices in academia and industry. It has been shown that …
A new varying-parameter convergent-differential neural-network for solving time-varying convex QP problem constrained by linear-equality
To solve online continuous time-varying convex quadratic-programming problems
constrained by a time-varying linear-equality, a novel varying-parameter convergent …
constrained by a time-varying linear-equality, a novel varying-parameter convergent …
Robust zeroing neural-dynamics and its time-varying disturbances suppression model applied to mobile robot manipulators
This paper proposes a novel robust zeroing neural-dynamics (RZND) approach as well as
its associated model for solving the inverse kinematics problem of mobile robot …
its associated model for solving the inverse kinematics problem of mobile robot …
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 …
Novel activation functions-based ZNN models for fixed-time solving dynamirc Sylvester equation
J Jin, J Zhu, J Gong, W Chen - Neural Computing and Applications, 2022 - Springer
A lot of research has validated that zeroing neural network (ZNN) model is a reliable tool for
solving time-varying problems. Generally, convergent performance is often one of the most …
solving time-varying problems. Generally, convergent performance is often one of the most …
Dynamic Moore–Penrose inversion with unknown derivatives: Gradient neural network approach
Finding dynamic Moore–Penrose inverses (DMPIs) in real-time is a challenging problem
due to the time-varying nature of the inverse. Traditional numerical methods for static Moore …
due to the time-varying nature of the inverse. Traditional numerical methods for static Moore …