Geometric and neuromorphic learning for nonlinear modeling, control and forecasting

Abstract

The author describes an algorithm based on results from computational geometry that learns nonlinear dynamical system mappings. The algorithm was applied to (a) the control of robot motion along a nominal trajectory on the basis of a learned model of its inverse dynamics, and (b) prediction of the behavior of a complex nonlinear dynamic system for forecasting regional electric power consumption on the basis of a model learned from noisy time series data. The algorithm is shown to compare favorably to a neural learning algorithm. >