Arthur E. Bryson

Arthur Earl Bryson Jr. (born October 7, 1925)[2] is the Paul Pigott Professor of Engineering Emeritus at Stanford University and the "father of modern optimal control theory". With Henry J. Kelley, he also pioneered an early version of the backpropagation procedure,[3][4][5] now widely used for machine learning and artificial neural networks.

Arthur E. Bryson
Born (1925-10-07) October 7, 1925
CitizenshipAmerican
Alma materCalifornia Institute of Technology
AwardsRufus Oldenburger Medal (1980)
Richard E. Bellman Control Heritage Award (1990)
Daniel Guggenheim Medal (2009)
Scientific career
FieldsControl theory
ThesisAn Interferometric Wind Tunnel Study of Transonic Flow past Wedge and Circular Arcs[1]
Doctoral advisorHans Wolfgang Liepmann[1]
Doctoral students

He was a member of the U.S. Navy V-12 program at Iowa State College, and received his B.S. in aeronautical engineering there in 1946.[6] He earned his Ph.D. from the California Institute of Technology, graduating in 1951. His thesis An Interferometric Wind Tunnel Study of Transonic Flow past Wedge and Circular Arcs was advised by Hans W. Liepmann.

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

Awards and honors

He was awarded membership into the National Academy of Engineering in 1970 and the National Academy of Sciences in 1973. He was awarded the IEEE Control Systems Science and Engineering Award in 1984,[7][8] the Richard E. Bellman Control Heritage Award in 1990 from the American Automatic Control Council[9] and the Daniel Guggenheim Medal in 2009.

References
  1. Arthur E. Bryson at the Mathematics 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 from the original on May 9, 2012. Retrieved May 6, 2012.
  7. "IEEE Control Systems Award Recipients" (PDF). IEEE. Retrieved January 15, 2011.
  8. "IEEE Control Systems Award". IEEE Control Systems Society. Archived from the original on 2010-12-29. Retrieved January 15, 2011.
  9. "Richard E. Bellman Control Heritage Award". American Automatic Control Council. Retrieved February 10, 2013.
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