ABSTRACT
INTRODUCTION Neural networks have proven to be a promising alternative to traditional techniques for nonlinear temporal prediction tasks (e.g., Curtiss, Brandemuehl, & Kreider, 1992; Lapedes & Farber, 1987; and Weigend, Huberman, & Rumelhart, 1992). However, temporal prediction is a particularly challenging problem because conventional neural net architectures and algorithms are not well suited for patterns that vary over time. The prototypical use of neural nets is in structural pattern recognition. In such a task, a collection of features-visual, semantic, or otherwise-is presented to a network and the network must categorize the input feature pattern as belonging to one or more classes. For example, a network might be trained to classify animal species based on a set of attributes describing living creatures such as “has tail,” “lives in water,” or “is carnivorous”; or a network could be trained to recognize visual patterns over a two-dimensional pixel array as a letter in {A , 2 3 ,..., Z}. In such tasks, the network is presented with all relevant information simultaneously.
