B
Bernard Widrow
Researcher at Stanford University
Publications - 174
Citations - 34318
Bernard Widrow is an academic researcher from Stanford University. The author has contributed to research in topics: Adaptive filter & Artificial neural network. The author has an hindex of 56, co-authored 171 publications receiving 33498 citations. Previous affiliations of Bernard Widrow include Gas Technology Institute & Massachusetts Institute of Technology.
Papers
More filters
Book
Adaptive Signal Processing
Bernard Widrow,Samuel D. Stearns +1 more
TL;DR: This chapter discusses Adaptive Arrays and Adaptive Beamforming, as well as other Adaptive Algorithms and Structures, and discusses the Z-Transform in Adaptive Signal Processing.
Journal ArticleDOI
Adaptive noise cancelling: Principles and applications
Bernard Widrow,J.R. Glover,John M. McCool,J. Kaunitz,C.S. Williams,R.H. Hearn,James R. Zeidler,Jr. Eugene Dong,R.C. Goodlin +8 more
TL;DR: It is shown that in treating periodic interference the adaptive noise canceller acts as a notch filter with narrow bandwidth, infinite null, and the capability of tracking the exact frequency of the interference; in this case the canceller behaves as a linear, time-invariant system, with the adaptive filter converging on a dynamic rather than a static solution.
Journal ArticleDOI
30 years of adaptive neural networks: perceptron, Madaline, and backpropagation
Bernard Widrow,Michael A. Lehr +1 more
TL;DR: The history, origination, operating characteristics, and basic theory of several supervised neural-network training algorithms (including the perceptron rule, the least-mean-square algorithm, three Madaline rules, and the backpropagation technique) are described.
Proceedings ArticleDOI
Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights
D. Nguyen,Bernard Widrow +1 more
TL;DR: The authors describe how a two-layer neural network can approximate any nonlinear function by forming a union of piecewise linear segments and a method is given for picking initial weights for the network to decrease training time.