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Arthur E. Bryson

From Wikipedia, the free encyclopedia
American aerospace and computer engineer
Arthur E. Bryson
Born (1925-10-07)October 7, 1925 (age 100)
Alma materCalifornia Institute of Technology
Awards
Scientific career
FieldsControl theory
Thesis An Interferometric Wind Tunnel Study ofTransonic Flow past Wedge and Circular Arcs[1]
Doctoral advisorHans Wolfgang Liepmann[1]
Doctoral students

Arthur Earl Bryson Jr. (born October 7, 1925)[2] is the Paul Pigott Professor of Engineering Emeritus atStanford University and the "father of modernoptimal control theory".[citation needed] WithHenry J. Kelley, he also pioneered an early version of thebackpropagation procedure,[3][4][5]now widely used formachine learning andartificial neural networks.

He was a member of the U.S. NavyV-12 program atIowa State College, and received hisB.S. in aeronautical engineering there in 1946.[6] He earned hisPh.D. from theCalifornia Institute of Technology, graduating in 1951. His thesisAn Interferometric Wind Tunnel Study of Transonic Flow past Wedge and Circular Arcs was advised byHans W. Liepmann.

Bryson was the Ph.D. advisor to the Harvard control theoristYu-Chi Ho.

In 1970, Bryson was elected a member of theNational Academy of Engineering for contributions to engineering education and imaginative application of modern statistical methods to engineering optimization.

Awards and honors

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He was awarded membership into theNational Academy of Engineering in 1970 and theNational Academy of Sciences in 1973. He was awarded theJohn R. Ragazzini Award in 1982 from theAmerican Automatic Control Council, theIEEE Control Systems Science and Engineering Award in 1984,[7][8] theRichard E. Bellman Control Heritage Award in 1990 from theAmerican Automatic Control Council[9] and theDaniel Guggenheim Medal in 2009.

References

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  1. ^abArthur E. Bryson at theMathematics Genealogy Project
  2. ^Journal of Dynamic Systems, Measurement, and Control. American Society of Mechanical Engineers. 1981. p. 1967.
  3. ^Arthur E. Bryson (1961, April). A gradient method for optimizing multi-stage allocation processes. In Proceedings of the Harvard Univ. Symposium on digital computers and their applications.
  4. ^Stuart Dreyfus (1990). Artificial Neural Networks, Back Propagation and the Kelley-Bryson Gradient Procedure. J. Guidance, Control and Dynamics, 1990.
  5. ^Jürgen Schmidhuber (2015). Deep learning in neural networks: An overview. Neural Networks 61 (2015): 85-117.ArXiv
  6. ^"Arthur E. Bryson, Jr".www.aere.iastate.edu. Archived fromthe original on May 9, 2012. RetrievedMay 6, 2012.
  7. ^"IEEE Control Systems Award Recipients"(PDF).IEEE. Archived fromthe original(PDF) on June 19, 2010. RetrievedJanuary 15, 2011.
  8. ^"IEEE Control Systems Award".IEEE Control Systems Society. Archived fromthe original on 2010-12-29. RetrievedJanuary 15, 2011.
  9. ^"Richard E. Bellman Control Heritage Award".American Automatic Control Council. Archived fromthe original on 2018-10-01. RetrievedFebruary 10, 2013.

External links

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