In this technical article, to seek the optimal solution to time-dependent nonlinear optimization
subject to linear inequality and equality constraints (TDNO-IEC), the gradient-based …

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 …

In engineering fields, time‐varying matrix inversion (TVMI) issue is often encountered.
Zeroing neural network (ZNN) has been extensively employed to resolve the TVMI problem …

As a type of recurrent neural networks (RNNs) modeled as dynamic systems, the gradient
neural network (GNN) is recognized as an effective method for static matrix inversion with …

Abstract The Black-Litterman (BL) model is a particularly essential analytical tool for effective
portfolio management in financial services sector since it enables investment analysts to …

This paper proposes a complex number representation method for dynamically recording
robot positions and develops an optimization strategy for measuring and minimizing inter …

The problem of solving algebraic Riccati equations (AREs) and certain linear matrix
equations which arise from the ARE frequently occur in applied and pure mathematics …

The hyperpower family of iterative methods with arbitrary convergence order is one of the
most used methods for estimating matrix inverses and generalized inverses, whereas the …

In the context of infinite-horizon optimal control problems, the algebraic Riccati equations
(ARE) arise when the stability of linear time-varying (LTV) systems is investigated. Using the …

Many researchers have investigated the time-varying (TV) matrix pseudoinverse problem in
recent years, for its importance in addressing TV problems in science and engineering. In …