Author(s)

A. T. Burrell, P. Papantoni-Kazakos

Computer Science Department, Oklahoma State University, Stillwater, USA.
Electrical Engineering Department, University of Colorado Denver, Denver, USA.

We consider qualitatively robust predictive mappings of stochastic environmental models, where protection against outlier data is incorporated. We utilize digital representations of the models and deploy stochastic binary neural networks that are pre-trained to produce such mappings. The pre-training is implemented by a back propagating supervised learning algorithm which converges almost surely to the probabilities induced by the environment, under general ergodicity conditions.

Qualitative Robustness; Predictive Model Mapping; Stochastic Approximation; Stochastic Binary Neural Networks; Real-Time Supervised Learning; Ergodicity

A. T. Burrell and P. Papantoni-Kazakos, "Stochastic Binary Neural Networks for Qualitatively Robust Predictive Model Mapping," International Journal of Communications, Network and System Sciences, Vol. 5 No. 9A, 2012, pp. 603-608. doi: 10.4236/ijcns.2012.529070.