B. Widrow | Semantic Scholar

Adaptive noise cancelling: Principles and applications

B. Widrow, J. Glover, R. Goodlin
Computer Science
Proceedings of the IEEE
24 March 1975

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.

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30 years of adaptive neural networks: perceptron, Madaline, and backpropagation

B. Widrow, Michael A. Lehr
Computer Science
Proc. IEEE
1 September 1990

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.

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Adaptive Signal Processing

B. Widrow, S. Stearns
Engineering, Computer Science
1985

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.

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Stationary and nonstationary learning characteristics of the LMS adaptive filter

B. Widrow, J. McCool, M. Larimore, C. Johnson
Computer Science
Proceedings of the IEEE
1 August 1976

It is shown that for stationary inputs the LMS adaptive algorithm, based on the method of steepest descent, approaches the theoretical limit of efficiency in terms of misadjustment and speed of adaptation when the eigenvalues of the input correlation matrix are equal or close in value.

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Adaptive switching circuits

B. Widrow, M. Hoff
Computer Science
1988

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The least mean fourth (LMF) adaptive algorithm and its family

E. Walach, B. Widrow
Computer Science
IEEE Trans. Inf. Theory
22 May 1984

It is possible, therefore, that a minimum mean fourth error algorithm can do a better job of least squares estimation than a mean square error algorithm.

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Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights

D. Nguyen, B. Widrow
Computer Science
IJCNN International Joint Conference on Neural…
17 June 1990

The authors describe how a two-layer neural network can approximate any nonlinear function by forming a union of piecewise linear segments. A method is given for picking initial weights for the…

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Adaptive antenna systems

B. Widrow, P. Mantey, L. Griffiths, B. Goode
Business
1 November 1967

The techniques described in this paper are applicable to signal‐receiving arrays for use over a wide range of frequencies and substantial reductions in noise reception are demonstrated in computer‐simulated experiments.

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Statistical theory of quantization

B. Widrow, I. Kollár, Ming-Chang Liu
Computer Science
1 April 1996

The theory behind the model, which describes the effect of uniform quantization by an additive noise that is uniformly distributed, uncorrelated with the input signal, and has a white spectrum is surveyed.

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Quantization Noise: Roundoff Error in Digital Computation, Signal Processing, Control, and Communications

B. Widrow, I. Kollár
Computer Science
21 July 2008

This authoritative analysis of quantization noise (roundoff error) invaluable and offers heuristic explanations along with rigorous proofs, making it easy to understand 'why' before the mathematical proof is given.