top
logo

Login Form



Visitors Counter

mod_vvisit_counterToday32
mod_vvisit_counterYesterday52
mod_vvisit_counterThis week333
mod_vvisit_counterThis month1532
mod_vvisit_counterAll155503

Who's Online

We have 12 guests online

Home Members Ioannis E. Livieris An improved weight-constrained neural network training algorithm
Error
  • Error loading feed data.
  • Error loading feed data.
An improved weight-constrained neural network training algorithm PDF Print E-mail

I.E. Livieris and P. Pintelas. An improved weight-constrained neural network training algorithm. Neural Computing and Applications, 2019.

 

 

Abstract - In this work, we propose an improved weight-constrained neural network training algorithm, named iWCNN. The proposed algorithm exploits the numerical efficiency of the L-BFGS matrices together with a gradient-projection strategy for handling the bounds on the weights. Additionally, an attractive property of iWCNN is that it utilizes a new scaling factor for defining the initial Hessian approximation used in the L-BFGS formula. Since the L-BFGS Hessian approximation is defined utilizing a small number of correction vector pairs our motivation is to further exploit them in order to increase the efficiency of the training algorithm and the convergence rate of the minimization process. The preliminary numerical experiments provide empirical evidence that the proposed training algorithm accelerates the training process.

 

Search Engines




bottom
top

Department of Mathematics

Educational Software News

Call for papers

Newest Education Titles


bottom

Designed by Ioannis E. Livieris. | Validate XHTML | CSS