Statistica: artificial neural networks for statistical data bounding
Benito Fernandez-Rodriguez, Gregory Dale Buckner · 2002
A novel family of artificial neural networks (ANNs), Statistica, has been developed to estimate the statistical bounds of normally-distributed, multiple-time sampled data. These networks utilize bilinear error cost functions that provide rapid, uniform convergence to the desired statistical bounds. Preliminary simulations using Statistica on two- and three-dimensional datasets clearly demonstrate the bounding capabilities of this ANN. Extensions to higher dimensional datasets are currently underway. Potential applications of Statistica include establishing confidence bounds for time-series forecasts and bounding gain trajectories in adaptive control systems.