Title

Heuristic Principles for the Design of Artificial Neural Networks

Document Type

Article

Publication Date

1-1-1999

Keywords

artificial neural networks, heuristics, input vector, hidden layer size, ANN learning method, design

Digital Object Identifier (DOI)

http://dx.doi.org/10.1016/s0950-5849(98)00116-5

Abstract

Artificial neural networks were used to support applications across a variety of business and scientific disciplines during the past years. Artificial neural network applications are frequently viewed as black boxes which mystically determine complex patterns in data. Contrary to this popular view, neural network designers typically perform extensive knowledge engineering and incorporate a significant amount of domain knowledge into artificial neural networks. This paper details heuristics that utilize domain knowledge to produce an artificial neural network with optimal output performance. The effect of using the heuristics on neural network performance is illustrated by examining several applied artificial neural network systems. Identification of an optimal performance artificial neural network requires that a full factorial design with respect to the quantity of input nodes, hidden nodes, hidden layers, and learning algorithm be performed. The heuristic methods discussed in this paper produce optimal or near-optimal performance artificial neural networks using only a fraction of the time needed for a full factorial design.

Comments

This article was written before Steven Walczak was affiliated with the University of South Florida.

Was this content written or created while at USF?

false

Citation / Publisher Attribution

Information and Software Technology, v. 41, issue 2, p. 107-117