accurate calculation of reliability index is very important for the reliability analysis of structures. in some limit state functions with nonlinear characteristic and several local optimum design points, the computational reliability methods may not appropriately determine the failure probability, and iterative reliability approaches may be converged to local optimum design points. in this pap...

Abstract This paper focuses on the problem of convex constraint nonlinear equations involving monotone operators in Euclidean space. A Fletcher and Reeves type derivative-free conjugate gradient method is proposed. The proposed designed to ensure descent property search direction at each iteration. Furthermore, convergence proved under assumption that underlying operator Lipschitz continuous. n...

In this paper, the Artificial Neural Network (ANN) approach is applied for forecasting groundwater level fluctuation in Aghili plain,southwest Iran. An optimal design is completed for the two hidden layers with four different algorithms: gradient descent withmomentum (GDM), levenberg marquardt (LM), resilient back propagation (RP), and scaled conjugate gradient (SCG). Rain,evaporation, relative...

The conjugated spectral gradient (SCG) method is an effective for non-constrained large-scale nonlinear optimization. In this work, a new conjugate proposed with strong Wolfe-Powell line search (SWP). proposal based on using the formula obtained by comparing algorithm previously published algorithms. Under usual assumptions, descent properties and overall global convergence of are proved. numer...

Abstract Machine learning algorithms can study existing data to perform specific tasks. One of the well-known machine is backpropagation algorithm, but this algorithm often provides poor convergence speed in training process and a long time. The purpose optimize standard using Beale-Powell conjugate gradient so that time needed achieve not too long, which later be used as reference information ...

In this study it has been tried, to compare results and convergence rate of sensitivity analysis and conjugate gradient algorithms to reduce fuel consumption and increasing engine performance by optimizing the timing of opening and closing valves in XU7/L3 engine. In this study, considering the strength and accuracy of simulation GT-POWER software in researches on the internal combustion engine...

Gradient descent parameter identification methods are typically effective for objective surfaces that are well conditioned, but can result in excessive computational cost or failed convergence when this is not the case. This research presents a novel Regional Conjugate Optimisation (RCO) approach, for parameter identification in three dimensions. The method characterises the objective surface p...

Many practical applications in applied sciences such as imaging, signal processing, and motion control can be reformulated into a system of nonlinear equations with or without constraints. In this paper, new descent projection iterative algorithm for solving convex constraints is proposed. The approach based on modified symmetric rank-one updating formula. search direction the proposed mimics b...

The original back-propagation methods were plagued with variable parameters which affected both the convergence properties of the training and the generalisation abilities of the resulting network. These parameters presented many difficulties when attempting to use these networks to solve particular mapping problems. A combination of established numerical minimisation methods (Polak-Ribiere Con...

We propose a new Riemannian geometry for fixed-rank matrices that is specifically tailored to the low-rank matrix completion problem. Exploiting the degree of freedom of a quotient space, we tune the metric on our search space to the particular least square cost function. At one level, it illustrates in a novel way how to exploit the versatile framework of optimization on quotient manifold. At ...