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 …

Industrial processes are typical cyber–physical–social systems (CPSSs), where the effective
management of employees and the efficient control of machines play important roles …

This article introduces an adaptive memristor-programming neurodynamic approach
(AMPNA) to tackle optimization problems that are nonconvex and nonsmooth with inequality …

The field of computer science has undergone rapid expansion due to the increasing interest
in improving system performance. This has resulted in the emergence of advanced …

Up to date, the existing methods for nonlinear optimization with time-dependent parameters
can be classified into two types: 1) static methods are capable of handling inequality …

While the recurrent neural network (RNN) has achieved remarkable performance on
dynamic and control tasks, its applications to image processing, particularly target detection …

Equality-constrained optimization problem captures increasing attention in the fields of
computer science, control engineering, and applied mathematics. Almost all of the relevant …

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 …

Zeroing neural network (ZNN) model is a powerful tool for solving time-varying nonlinear
minimization problems. This study presents some limitations of existing ZNN models, mainly …

This article considers the distributed time-varying optimal resource allocation problem with
time-varying quadratic cost functions and a time-varying coupled equality constraint for …