Continuous-time recursive least-squares estimation, adaptive neuralnetworks and systolic arrays

摘要

We derive square-root covariance-type and information-type algorithms for continuous-time recursive least-squares estimation. The algorithms allow for easy manipulation and uniform parallelization. They are related to well-known neural adaptation laws and can be considered as continuous-time limits of systolic arrays