First, the kernel Kalman rule (KKR) is presented as an approximate alternative to the KBR that estimates the embedding of the state based on a recursive least squares objective. Based on the KKR we present the kernel Kalman filter (KKF) that updates an embedding of the belief state and ...
least squares (redirected fromRecursive least squares algorithm) Thesaurus Medical least squares pl.n.Statistics A method of determining the curve that best describes the relationship between expected and observed sets of data by minimizing the sums of the squares of deviation between observed and expec...
Recursive least squares ... Q Guo,X Zhao,Y Sun - Eighth International Conference on Electrical Machines & Systems 被引量: 5发表: 2006年 The Recursive Algorithm of H^|^infin; Control Problems for Standard and Nonstandard Singularly Perturbed Systems Making use of the recursive technique, we ...
This paper derives the recursive formulas of the computation of the criterion functions for the well-known weighted recursive least squares algorithm and the finite-data-window recursive least squares algorithm for linear regressive models. The analysis indicates that the proposed recursive computation form...
"A recursive least squares approach to the on-line adaptive control problem", Int. j. Control, Vol. 16, pp. 243-260, 1972.D. J. Sandoz and B. H. Swanick, "A recursive least squares approach to the on-line adaptive control problem", Int. j. Control, vol. 16,pp.243*260, 1972...
“tuple slicer" in order to utilise the setin the CVPP framework. However this algorithm introduces a new question, namely what size of tuples should be considered during this algorithm. Figure4attempts to give some preliminary experimental evidence on this problem. However, a theoretical analysis...
Sketch RLS is an adaptive filtering algorithm that brings sketching ideas into the classical recursive least squares algorithm. This is the python implementation of the algorithm. - LCAV/sketchrls
Triplet loss is a type of learning algorithm used in machine learning. It is called "triplet loss" because it uses three things to help a computer learn: a positive example, a negative example, and a "anchor" point. Imagine you have a computer that is trying to learn what a cat looks...
The concept of underdetermined recursive least-squares (URLS) adaptive filtering is introduced. In particular, the URLS algorithm is derived and shown to be a direct consequence of the principle of minimal disturbance. By exploiting the Hankel structure of the filter input matrix, the fast transversa...
In the papers (Kaminskas, 1972; Kaminskas and Nemura, 1975; Yin, 1989) the stopping rules of recursive least squares (RLS) are worked out using the ellipsoidal confidence region for the respective parameter vector of a linear dynamic system. The aim of the given paper is the development of...