Fig. 5. Comparison of the SRM-based algorithms for the " Bad " channel, SNR = 10 dB. SG: Standard stochastic gradient algorithm. Norm: Normalized stochastic gradient algorithm. SRM: Off-line SRM algorithm of [3], where S k is obtained at time k by averaging all of the data through time k to estimate ensemble averages. Conj Grad: Conjugate gradient algorithm. The results are averaged over 200 sa...

In theory, the successive gradients generated by the conjugate gradient method applied to a quadratic should be orthogonal. However, for some ill-conditioned problems, orthogonality is quickly lost due to rounding errors, and convergence is much slower than expected. A limited memory version of the nonlinear conjugate gradient method is developed. The memory is used to both detect the loss of o...

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...

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...

This document describes Version of the conjugate gradient solver component of DAO s Physical space Statistical Analysis System PSAS An overview of the general PSAS algorithm is presented followed by an outline of the pre conditioned conjugate gradient algorithm and its implementation in PSAS A description of the main For tran subroutines related to the conjugate gradient solver is given with th...

In this paper we consider a constrained optimization problem where the objectives are fuzzy functions (fuzzy-valued functions). Fuzzy constrained Optimization (FO) problem plays an important role in many fields, including mathematics, engineering, statistics and so on. In the other side, in the real situations, it is important to know how may obtain its numerical solution of a given interesting...

A preconditioned conjugate gradient method was implemented into an iteration on a program for data estimation of breeding values, and its convergence characteristics were studied. An algorithm was used as a reference in which one fixed effect was solved by Gauss-Seidel method, and other effects were solved by a second-order Jacobi method. Implementation of the preconditioned conjugate gradient ...