Heuristic Principles for the Design of Artificial Neural Networks
Document Type
Article
Publication Date
1-1999
Keywords
artificial neural networks, heuristics, input vector, hidden layer size, ANN learning method, design
Digital Object Identifier (DOI)
https://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.
Was this content written or created while at USF?
No
Citation / Publisher Attribution
Information and Software Technology, v. 41, issue 2, p. 107-117
Scholar Commons Citation
Walczak, Steven and Cerpa, Narciso, "Heuristic Principles for the Design of Artificial Neural Networks" (1999). School of Information Faculty Publications. 211.
https://digitalcommons.usf.edu/si_facpub/211
