Localized Adaptive Bounds for Online Approximation Based Control

This article develops new methods for adaptively bounding approximation accuracy with methods that involve localized forgetting. The existing results use global forgetting. The importance of local versus global forgetting is motivated in the text. Such bounds have utility for self-organizing approximators that could adjust the number of basis elements N by adding additional approximation resources in the regions where the approximation error bound is large.