title: Efficient algorithms for function approximation with piecewise linear sigmoidal networks.
abstract: This paper presents a computationally efficient algorithm for function approximation with piecewise linear sigmoidal nodes. A one hidden layer network is constructed one node at a time using the well-known method of fitting the residual. The task of fitting an individual node is accomplished using a new algorithm that searches for the best fit by solving a sequence of quadratic programming problems. This approach offers significant advantages over derivative-based search algorithms (e.g., backpropagation and its extensions). Unique characteristics of this algorithm include: finite step convergence, a simple stopping criterion, solutions that are independent of initial conditions, good scaling properties and a robust numerical implementation. Empirical results are included to illustrate these characteristics.
authors.affiliation: Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA.
authors.forename: D R
authors.initials: DR
authors.lastname: Hush
authors.forename: B
authors.initials: B
authors.lastname: Horne