This paper reports the study results on neural network training algorithm of numerical optimization techniques multiface detection in static images. The training algorithms involved are scale gradient conjugate backpropagation, conjugate gradient backpropagation with Polak-Riebre updates, conjugate gradient backpropagation with Fletcher-Reeves updates, one secant backpropagation and resilent ba...

Global Krylov subspace methods are the most efficient and robust methods to solve generalized coupled Sylvester matrix equation. In this paper, we propose the nested splitting conjugate gradient process for solving this equation. This method has inner and outer iterations, which employs the generalized conjugate gradient method as an inner iteration to approximate each outer iterate, while each...

Iterative methods for nonlinear monotone equations do not require the differentiability assumption on residual function. This special property of makes them suitable solving large-scale nonsmooth equations. In this work, we present a diagonal Polak-Ribi\begin{document}$ \grave{e} $\end{document}re-Polyak ...

Abstract: The existing literature predominantly concentrates on the utilization of the gradient descent algorithm for control systems’ design in power systems for stability enhancement. In this paper, various flavors of the Conjugate Gradient (CG) algorithm have been employed to design the online neuro-fuzzy linearization-based adaptive control strategy for Line Commutated Converters’ (LCC) Hig...

This paper presents the Hager–Zhang (HZ)-type Riemannian conjugate gradient method that uses exponential retraction. We also present global convergence analyses of our proposed under two kinds assumptions. Moreover, we numerically compare methods with existing by solving optimization problems on unit sphere. The numerical results show has much better performance than methods, i.e., FR, DY, PRP,...