A conjugate gradient based method for Decision Neural Network training

SAGRAD: A Program for Neural Network Training with Simulated Annealing and the Conjugate Gradient Method

SAGRAD (Simulated Annealing GRADient), a Fortran 77 program for computing neural networks for classification using batch learning, is discussed. Neural network training in SAGRAD is based on a combination of simulated annealing and Møller's scaled conjugate gradient algorithm, the latter a variation of the traditional conjugate gradient method, better suited for the nonquadratic nature of neura...

A Spectral Version of Perry’s Conjugate Gradient Method for Neural Network Training

In this work, an efficient training algorithm for feedforward neural networks is presented. It is based on a scaled version of the conjugate gradient method suggested by Perry, which employs the spectral steplength of Barzilai and Borwein that contains second order information without estimating the Hessian matrix. The learning rate is automatically adapted at each epoch, using the conjugate gr...

Conjugate gradient algorithm for training neural networks

A New Hybrid Conjugate Gradient Method Based on Eigenvalue Analysis for Unconstrained Optimization Problems

In this paper‎, ‎two extended three-term conjugate gradient methods based on the Liu-Storey ({tt LS})‎ ‎conjugate gradient method are presented to solve unconstrained optimization problems‎. ‎A remarkable property of the proposed methods is that the search direction always satisfies‎ ‎the sufficient descent condition independent of line search method‎, ‎based on eigenvalue analysis‎. ‎The globa...

Conjugate Gradient Methods in Training Neural Networks

Training of artificial neural networks is normally a time consuming task due to iterative search imposed by the implicit nonlinearity of the network behavior. To tackle the supervised learning of multilayer feed forward neural networks, the backpropagation algorithm has been proven to be one of the most successful neural network algorithm. Although backpropagation training has proved to be effi...

A Conjugate Gradient-neural Network Technique for Ultrasound Inverse Imaging

In this paper, a new technique for solving the two-dimensional inverse scattering problem for ultrasound inverse imaging is presented. Reconstruction of a two-dimensional object is accomplished using an iterative algorithm which combines the conjugate gradient (CG) method and a neural network (NN) approach. The neural network technique is used to exploit knowledge of the statistical characteris...