Neural networks for function approximation in nonlinear controlTwo neural network architectures are compared with a classical spline interpolation technique for the approximation of functions useful in a nonlinear control system. A standard back-propagation feedforward neural network and a cerebellar model articulation controller (CMAC) neural network are presented, and their results are compared with a B-spline interpolation procedure that is updated using recursive least-squares parameter identification. Each method is able to accurately represent a one-dimensional test function. Tradeoffs between size requirements, speed of operation, and speed of learning indicate that neural networks may be practical for identification and adaptation in a nonlinear control environment.
Document ID
19910045449
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Linse, Dennis J. (Princeton Univ. NJ, United States)
Stengel, Robert F. (Princeton University NJ, United States)