the spatial distribution of petrophysical properties within the reservoirs is one of the most importantfactors in reservoir characterization. flow units are the continuous body over a specific reservoirvolume within which the geological and petrophysical properties are the same. accordingly, anaccurate prediction of flow units is a major task to achieve a reliable petrophysical description of a...

The conjugate gradient optimization algorithm is combined with the modified back propagation algorithm to yield a computationally efficient algorithm for training multilayer perceptron (MLP) networks (CGFR/AG). The computational efficiency is enhanced by adaptively modifying initial search direction as described in the following steps: (1) Modification on standard back propagation algorithm by ...

A new constant modulus blind equalization based on direction gradient algorithm was proposed, which can obtain robust convergence performance under impulse noise environment. The impulse noise has no more than two order moments, so constant modulus algorithm (CMA) based on stochastic gradient descent algorithm is often ill-convergence or divergence. The direction gradient algorithm uses the rel...

Sparse representation model adopts an image patch as a linear combination of a few atoms chosen out from an overcomplete dictionary, and they have shown promising results in various image restoration applications. Gradient feature is important structure information in image processing. Their combination is not sensitive to noise, which can improve and enhance the accuracy of similarity measure,...

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 this paper, we propose an efficient algorithm for estimating the natural policy gradient using parameter-based exploration; this algorithm samples directly in the parameter space. Unlike previous methods based on natural gradients, our algorithm calculates the natural policy gradient using the inverse of the exact Fisher information matrix. The computational cost of this algorithm is equal t...

The conjugate gradient (CG) method is a popular Krylov space method for solving systems of linear equations of the form Ax = b, where A is a symmetric positive-deenite matrix. This method can be applied regardless of whether A is dense or sparse. In this paper, we show how restructuring compiler technology can be applied to transform a sequential, dense matrix CG program into a parallel, sparse...

This paper applies data selective updating to the Modified Conjugate Gradient algorithm. In search for a new conjugategradient-like filtering algorithm, two different approaches are developed: the first one results in the recently proposed set-membership affine projection (SM-AP) algorithm and the second one reduces the computational requirements of the modified congujate gradient algorithm whi...

A complex-valued nonlinear gradient descent (CNGD) learning algorithm for a simple finite impulse response (FIR) nonlinear neural adaptive filter with an adaptive amplitude of the complex activation function is proposed. This way the amplitude of the complex-valued analytic nonlinear activation function of a neuron in the learning algorithm is made gradient adaptive to give the complex-valued a...