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 …

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 …

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 …

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 …

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 …

This paper presents a new neurodynamic approach for solving the constrained
pseudoconvex optimization problem based on more general assumptions. The proposed …

This paper proposes a feedback neural network model for solving convex nonlinear
programming (CNLP) problems. Under the condition that the objective function is convex …

This paper presents a capable neural network for solving strictly convex quadratic
programming (SCQP) problems with general linear constraints. The proposed neural …

The last four decades have witnessed the birth and growth of neurodynamic optimization
with numerous recurrent neural networks developed for solving various constrained …

This paper presents a dynamic optimization scheme for solving degenerate convex
quadratic programming (DCQP) problems. According to the saddle point theorem …