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Bernard Widrow

Bernard Widrow (born December 24, 1929) is a U.S. professor of electrical engineering at Stanford University.[1] He is the co-inventor of the Widrow–Hoff least mean squares filter (LMS) adaptive algorithm with his then doctoral student Ted Hoff.[2] The LMS algorithm led to the ADALINE and MADALINE artificial neural networks and to the backpropagation technique. He made other fundamental contributions to the development of signal processing in the fields of geophysics, adaptive antennas, and adaptive filtering. A summary of his work is.[3]

Bernard Widrow

(1929-12-24) December 24, 1929

American

Massachusetts Institute of Technology[1]

Electrical engineering

Stanford University

William Linvill

He is the namesake of "Uncle Bernie's Rule": the training sample size should be 10 times the number of weights in a network.[4][5]

1965 "A critical comparison of two kinds of adaptive classification networks", K. Steinbuch and B. Widrow, IEEE Transactions on Electronic Computers, pp. 737–740.

1985 B. Widrow and S. D. Stearns. Adaptive Signal Processing. New Jersey: Prentice-Hall, Inc., 1985.

1994 B. Widrow and E. Walach. Adaptive Inverse Control. New Jersey: Prentice-Hall, Inc., 1994.

2008 B. Widrow and I. Kollar. Quantization Noise: Roundoff Error in Digital Computation, Signal Processing, Control, and Communications. Cambridge University Press, 2008.

Elected Fellow , 1976[2]

IEEE

Elected Fellow AAAS, 1980

[2]

1984[2]

IEEE Centennial Medal

1986[2]

IEEE Alexander Graham Bell Medal

IEEE Neural Networks Pioneer Medal, 1991

[2]

Inducted into the , 1995

National Academy of Engineering

IEEE Signal Processing Society Award, 1999

IEEE Millennium Medal, 2000

2001[14]

Benjamin Franklin Medal

International Neural Network Society (INNIS) Board member 2004

He was one of the Board of Governors of the International Neural Network Society (INNIS) in 2003.