{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T09:55:07Z","timestamp":1777715707509,"version":"3.51.4"},"reference-count":19,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2000,1,1]],"date-time":"2000-01-01T00:00:00Z","timestamp":946684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2000,1]]},"abstract":"<jats:p>A new adaptive neural network controller for robots is presented. The controller is based on direct adaptive techniques. Unlike many neural network controllers in the literature, inverse dynamical model evaluation is not required. A numerically robust, computationally efficient processing scheme for neural network weight estimation is described, namely, the inverse QR decomposition (INVQR). The inverse QR decomposition and a weighted recursive least-squares (WRLS) method for neural network weight estimation is derived using Cholesky factorization of the data matrix. The algorithm that performs the efficient INVQR of the underlying space-time data matrix may be implemented in parallel on a triangular array. Furthermore, its systolic architecture is well suited for VLSI implementation. Another important benefit of the INVQR decomposition is that it solves directly for the time-recursive least-squares filter vector, while avoiding the sequential back-substitution step required by the QR decomposition approaches.<\/jats:p>","DOI":"10.1177\/02783640022066725","type":"journal-article","created":{"date-parts":[[2003,7,19]],"date-time":"2003-07-19T02:59:46Z","timestamp":1058583586000},"page":"32-41","source":"Crossref","is-referenced-by-count":0,"title":["A Fast New Algorithm for a Robot Neurocontroller Using Inverse QR Decomposition"],"prefix":"10.1177","volume":"19","author":[{"given":"A. S.","family":"Morris","sequence":"first","affiliation":[{"name":"Department of Automatic Control and Systems Engineering, University of                         Sheffield, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"Khemaissia","sequence":"additional","affiliation":[{"name":"Department of Electronics, College of Technology at Riyadh, Riyadh 11551,                         Kingdom of Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2000,1,1]]},"reference":[{"key":"atypb1","doi-asserted-by":"crossref","unstructured":"Ahmed, M. S., and Anjum, M. F. 1997. Neural-net-based direct self-tuning control of nonlinear plants . Intl. J. Control 2: 85\u2013104 .","DOI":"10.1080\/002071797224838"},{"key":"atypb2","doi-asserted-by":"crossref","unstructured":"Arimoto, S., and Naniwa, T., 1992 (Nice, France). Learning control for robot tasks under geometric end-point constraints . 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