A systolic algorithm for adaptive set membership identification

An adaptive set membership identification algorithm with a very flexible forgetting scheme is presented. In preliminary experiments, the method yields highly accurate estimates using very few of the data, and quickly adapts to fast-changing dynamics. A compact systolic architecture to implement this algorithm is developed which uses O (m) cells and reduces the computational complexity to O(m) operations per observation, where m represents the number of parameters to be estimated in a linear system or signal model