Controlling and processing of time-variant problem is universal in the fields of engineering
and science, and the discrete-time recurrent neural network (RNN) model has been proven …

Many complex nonlinear optimization or control issues can be transformed into the solving
of time-varying nonlinear equations (TVNEs), playing a fundamental role in the control and …

It is generally considered that controlling a robot precisely becomes tough on the condition
of unknown structure information. Applying a data-driven approach to the robot control with …

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 …

An echo state network (ESN) draws widespread attention and is applied in many scenarios.
As the most typical approach for solving the ESN, the matrix inverse operation of high …

Time-variant problems are widespread in science and engineering, and discrete-time
recurrent neurodynamics (DTRN) method has been proved to be an effective way to deal …

Time-dependent nonlinear equation (TDNE) arises in numerous engineering applications.
Recently, zeroing neural network (ZNN) has been proven to be an effective alternative for …

Autonomous driving systems are inevitably influenced by kinds of noise, which may further
incur unwanted safety problems. In this paper, a polynomial noise resilience neural …

Various recurrent neural network (RNN) models, especially zeroing neutral network (ZNN)
models, have been investigated to solve time-varying linear inequalities (TVLI) and applied …

This article develops a fuzzy-neural controller for the kinematic and collaborative control of
multimanipulator systems. The entire control scheme is designed based on quadratic …