Alternation minimization training of radial basis function networks

Peter T. Szymanski; Michael D. Lemmon

doi:10.1117/12.235923

22 March 1996 Alternation minimization training of radial basis function networks

Peter T. Szymanski, Michael D. Lemmon

Proceedings Volume 2760, Applications and Science of Artificial Neural Networks II; (1996) https://doi.org/10.1117/12.235923
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

Radial basis function (RBF) networks and their training have garnered much interest in the neural computing literature. Current training methods resemble Expectation-Maximization (EM) techniques and optimize network performance by alternately minimizing performance measures with respect to disjoint parameter sets. Since the number of RBF parameters and the training data sets may be large, efficient training methods are imperative for these networks' practical use. This paper presents two hybrid interior point (IP) training algorithms based upon solvers for linear programming (LP) problems. Presented are small-step and large-step path following algorithms which implement an alternating minimization. The small-step algorithm converges to (epsilon) -neighborhoods of locally optimal solutions with a theoretical O((root)nlog₂((root)n/(epsilon) )) iteration rate and O(n^3.5log₂((root)n/(epsilon) )) floating point operation (flop) cost. It exhibits an empirical cost of approximately O(n²log₂(1/(epsilon) )) flops where n is the dimension of an associated LP problem. The large-step algorithm exhibits a constant convergence rate and approximately O(n^1.5log₂(1/(epsilon) )) flop cost. IP/EM algorithm comparison show that the IP methods exhibit lower order computational properties, and that for some problems the large-step algorithm outperforms the EM algorithm. Also presented are results demonstrating the small-step algorithm's use for training on a system identification problem.

Citation Download Citation

Peter T. Szymanski and Michael D. Lemmon "Alternation minimization training of radial basis function networks", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); https://doi.org/10.1117/12.235923

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
12 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Expectation maximization algorithms

Data modeling

Stochastic processes

Algorithms

Optimization (mathematics)

Computer programming

System identification

Show All Keywords

Keywords/Phrases

Search In:

Publication Years