The so-called zeroing neural network (ZNN) is an effective recurrent neural network for
solving dynamic problems including the dynamic nonlinear equations. There exist numerous …

The optimal formation problem of multirobot systems is solved by a recurrent neural network
in this paper. The desired formation is described by the shape theory. This theory can …

Recent decades have witnessed a trend that control-theoretical techniques are widely
leveraged in various areas, eg, design and analysis of computational models …

The aim of this paper is to introduce a new recurrent neural network to solve linear
programming. The main characteristic of the proposed scheme is its design based on the …

We focus on the fixed-time convergence and robustness of gradient-based dynamic
networks for solving convex optimization. Most of the existing gradient-based dynamic …

This paper focuses on finite-time recurrent neural networks with continuous but non-smooth
activation function solving nonlinearly constrained optimization problems. Firstly, definition …

Methods, systems, and apparatus, including computer programs encoded on a computer
storage medium, for a projection neural network. In one aspect, a projection neural network …

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 …

Nowadays, there are time-critical applications involving linear equations, such as the fault
reconstruction problem, where hard response time constraints and robustness to external …

To cope with increasing, challenging requirements and shorter development cycles, more
complex, often nonlinear, systems with high interactions have to be optimized in many fields …