Application of neural network models for mathematical programming problems: a state of art review
K Lachhwani - Archives of Computational Methods in Engineering, 2020 - Springer
Artificial neural networks or neural networks (NN) are new computational models based on
the working of biological neurons of human body. A NN model consists of an interactive …
the working of biological neurons of human body. A NN model consists of an interactive …
A deep learning approach for solving linear programming problems
Finding the optimal solution to a linear programming (LP) problem is a long-standing
computational problem in Operations Research. This paper proposes a deep learning …
computational problem in Operations Research. This paper proposes a deep learning …
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 …
Predefined-time convergent neural solution to cyclical motion planning of redundant robots under physical constraints
W Li - IEEE Transactions on Industrial Electronics, 2019 - ieeexplore.ieee.org
In industrial activities, robot arms are usually requested to perform repetitive tasks. As a
special recurrent neural network, zeroing neural network (ZNN) has been successfully …
special recurrent neural network, zeroing neural network (ZNN) has been successfully …
A neurodynamic approach to nonsmooth constrained pseudoconvex optimization problem
This paper presents a new neurodynamic approach for solving the constrained
pseudoconvex optimization problem based on more general assumptions. The proposed …
pseudoconvex optimization problem based on more general assumptions. The proposed …
A dynamic system model for solving convex nonlinear optimization problems
AR Nazemi - Communications in Nonlinear Science and Numerical …, 2012 - Elsevier
This paper proposes a feedback neural network model for solving convex nonlinear
programming (CNLP) problems. Under the condition that the objective function is convex …
programming (CNLP) problems. Under the condition that the objective function is convex …
A neural network model for solving convex quadratic programming problems with some applications
A Nazemi - Engineering Applications of Artificial Intelligence, 2014 - Elsevier
This paper presents a capable neural network for solving strictly convex quadratic
programming (SCQP) problems with general linear constraints. The proposed neural …
programming (SCQP) problems with general linear constraints. The proposed neural …
A Survey of Neurodynamic Optimization
Y Xia, Q Liu, J Wang, A Cichocki - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The last four decades have witnessed the birth and growth of neurodynamic optimization
with numerous recurrent neural networks developed for solving various constrained …
with numerous recurrent neural networks developed for solving various constrained …
A capable neural network framework for solving degenerate quadratic optimization problems with an application in image fusion
A Nazemi - Neural Processing Letters, 2018 - Springer
This paper presents a dynamic optimization scheme for solving degenerate convex
quadratic programming (DCQP) problems. According to the saddle point theorem …
quadratic programming (DCQP) problems. According to the saddle point theorem …