title: Selecting optimal experiments for multiple output multilayer perceptrons.
abstract: Where should a researcher conduct experiments to provide training data for a multilayer perceptron? This question is investigated, and a statistical method for selecting optimal experimental design points for multiple output multilayer perceptrons is introduced. Multiple class discrimination problems are examined using a framework in which the multilayer perceptron is viewed as a multivariate nonlinear regression model. Following a Bayesian formulation for the case where the variance-covariance matrix of the responses is unknown, a selection criterion is developed. This criterion is based on the volume of the joint confidence ellipsoid for the weights in a multilayer perceptron. An example is used to demonstrate the superiority of optimally selected design points over randomly chosen points, as well as points chosen in a grid pattern. Simplification of the basic criterion is offered through the use of Hadamard matrices to produce uncorrelated outputs.
authors.affiliation: Department of Operational Sciences, Department of the Air Force, Air Force Institute of Technology, Wright Patterson AFB, OH 45433-7765, USA.
authors.forename: L M
authors.initials: LM
authors.lastname: Belue
authors.forename: K W
authors.initials: KW
authors.lastname: Bauer
authors.forename: D W
authors.initials: DW
authors.lastname: Ruck
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D004192:Discrimination, Psychological; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics