Robust control techniques require a dynamic model of the plant and bounds on model uncertainty to formulate control laws with guaranteed stability. Although techniques for modeling and identifying dynamic systems are well established, very few procedures exist for estimating uncertainty bounds. In the case of H-infinity control synthesis, a conservative weighting function for model uncertainty is usually chosen to ensure closed-loop stability over the entire operating space. The primary drawback of this conservative, "hard computing" approach is reduced performance due to the number of plants the resulting controller can stabilize. This paper demonstrates a novel "soft computing" approach to estimate bounds of model uncertainty resulting from parameter variations, unmodeled dynamics, and non-deterministic processes in dynamic plants. This approach uses confidence interval networks (CINs), radial basis function neural networks trained using asymmetric bilinear error cost functions, to estimate confidence intervals on the uncertainty associated with nominal linear models for robust control synthesis. This research couples the hard computing features of H-infinity control with the soft computing characteristics of intelligent system identification, and realizes the combined advantages of both. Experimental demonstrations conducted on a multivariable, flexible-rotor active magnetic bearing system confirm these capabilities.