Training of supervised neural networks via a nonlinear primal-dual interior-point method

Author

Trafalis, Theodore B. ; Couellan, Nicolas P. ; Bertrand, S?©bastien C.

Author_Institution

Sch. of Ind. Eng., Oklahoma Univ., Norman, OK, USA

Volume

3

fYear

1997

Firstpage

2017

Abstract

We propose a new training algorithm for feedforward supervised neural networks based on a primal-dual interior-point method for nonlinear programming. Specifically, we consider a one-hidden layer network architecture where the error function is defined by the L₂ norm and the activation function of the hidden and output neurons is nonlinear. Computational results are given for odd parity problems with 2, 3, and 5 inputs respectively. Approximation of a nonlinear dynamical system is also discussed

Keywords

duality (mathematics); learning (artificial intelligence); neural net architecture; nonlinear programming; feedforward supervised neural networks; nonlinear activation function; nonlinear dynamical system; nonlinear primal-dual interior-point method; nonlinear programming; odd parity problems; one-hidden layer network architecture; training algorithm; Computer architecture; Feedforward neural networks; Industrial engineering; Industrial training; Jacobian matrices; Lagrangian functions; Linear programming; Neural networks; Neurons; Nonlinear dynamical systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks,1997., International Conference on

Print_ISBN

0-7803-4122-8

Type

conf

DOI

10.1109/ICNN.1997.614210

Filename

614210