Cited by 4,009Analog and Mixed-Signal Circuit DesignAdvanced Memory and Neural ComputingVLSI and FPGA Design Techniques

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ADAPTIVE SWITCHING CIRCUITS

WIDROW, B., HOFF, M. E.·1960

DOI 10.21236/ad0241531

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Models of Learning Systems.
Buchanan, B. G., Mitchell, T. M., Smith, R. G., & Johnson, C. R. (1979) · Defense Technical Information Center (DTIC) · Cited by 74
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Lanzi, P. L. (1997) · Cited by 65
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Rossi, R. G., Lopes, A. D. A., & Rezende, S. O. (2015) · Information Processing & Management · doi:10.1016/j.ipm.2015.07.004 · Cited by 64
Chapter 2 Computational aspects of motor control and motor learning
Jordan, M. I. (1996) · Handbook of perception and action · doi:10.1016/s1874-5822(06)80005-8 · Cited by 69
RETIN: A Content-Based Image Indexing and Retrieval System
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Lanzi, P. L., Loiacono, D., Wilson, S. W., & Goldberg, D. E. (2007) · Evolutionary Computation · doi:10.1162/evco.2007.15.2.133 · Cited by 64
Frequency domain tracking characteristics of adaptive algorithms
Gunnarsson, S., & Lennart Ljung, A. (1989) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/29.32284 · Cited by 64
A comparison between criterion functions for linear classifiers, with an application to neural nets
Barnard, E., & Casasent, D. P. (1989) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.44018 · Cited by 64
Machine Learning for Audio, Image and Video Analysis
Camastra, F., & Vinciarelli, A. (2007) · Springer eBooks · doi:10.1007/978-1-84800-007-0 · Cited by 64
Zero intelligence in economics and finance
Ladley, D. (2012) · The Knowledge Engineering Review · doi:10.1017/s0269888912000173 · Cited by 63
Spiking Neural Networks: Principles and Challenges
Grüning, A., & Bohté, S. M. (2014) · Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands · Cited by 63
Hebb synaptic plasticity
VIANADIPRISCO, G. (1984) · Progress in Neurobiology · doi:10.1016/0301-0082(84)90021-2 · Cited by 65
LMS learning algorithms: misconceptions and new results on converence
Wang, Z.-Q., MANRY, M. T., & Schiano, J. L. (2000) · IEEE Transactions on Neural Networks · doi:10.1109/72.822509 · Cited by 64
Using Spatio-temporal Correlations to Learn Invariant Object Recognition
Wallis, G. M. (1996) · Neural Networks · doi:10.1016/s0893-6080(96)00041-x · Cited by 63
Mobile robot visual mapping and localization: A view-based neurocomputational architecture that emulates hippocampal place learning
Bachelder, I. A., & Waxman, A. M. (1994) · Neural Networks · doi:10.1016/s0893-6080(05)80160-1 · Cited by 64
Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses
Lin, Y., Bennett, C. H., Cabaret, T., Vodenicarevic, D., Chabi, D., Querlioz, D., Jousselme, B., Derycke, V., & Klein, J. (2016) · Scientific Reports · doi:10.1038/srep31932 · Cited by 64
Deep learning in hospitality and tourism: a research framework agenda for future research
Essien, A. E., & Chukwukelu, G. (2022) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-09-2021-1176 · Cited by 73
A stochastic version of the delta rule
Hanson, S. J. (1990) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(90)90081-y · Cited by 65
Reinforcement learning of goal-directed obstacle-avoiding reaction strategies in an autonomous mobile robot
Millán, J. D. R. (1995) · Robotics and Autonomous Systems · doi:10.1016/0921-8890(95)00021-7 · Cited by 61
An overview of the CMAC neural network
Glanz, F. H., Miller, W. T., & Kraft, L. G. (2002) · IEEE Conference on Neural Networks for Ocean Engineering · doi:10.1109/icnn.1991.163366 · Cited by 62
Multi-stage cognitive map for failures assessment of production processes: An extension in structure and algorithm
Rezaee, M. J., Yousefı, S., & Babaei, M. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.10.069 · Cited by 62
An Introduction to Neural Networks
Aggarwal, C. C. (2018) · Springer eBooks · doi:10.1007/978-3-319-94463-0_1 · Cited by 72
Application of neural networks to computer recipe prediction
Bishop, J. M., Bushnell, M. J., & Westland, S. (1991) · Color Research & Application · doi:10.1002/col.5080160104 · Cited by 62
Power quality event detection using Adaline
Abdel-Galil, T. K., El-Saadany, E. F., & Salama, M. M. A. (2002) · Electric Power Systems Research · doi:10.1016/s0378-7796(02)00173-6 · Cited by 62
Introduction to artificial neural systems for pattern recognition
Burke, L. I. (1991) · Computers & Operations Research · doi:10.1016/0305-0548(91)90091-5 · Cited by 62
Neural networks for decision support:
Schocken, S., & Ariav, G. (1994) · Decision Support Systems · doi:10.1016/0167-9236(94)90015-9 · Cited by 62
Optical associatron: a simple model for optical associative memory
Ishikawa, M., Mukohzaka, N., Toyoda, H., & Suzuki, Y. (1989) · Applied Optics · doi:10.1364/ao.28.000291 · Cited by 62
Generalized Inverse Approach to Adaptive Multiclass Pattern Classification
Wee, W. G. (1968) · IEEE Transactions on Computers · doi:10.1109/tc.1968.226881 · Cited by 62
Common Mechanisms in Infant and Adult Category Learning
Gureckis, T. M., & Love, B. C. (2004) · Infancy · doi:10.1207/s15327078in0502_4 · Cited by 61

Authors: WIDROW, B., & HOFF, M. E.
Publisher: Defense Technical Information Center
Published: 1960-06-30
DOI: 10.21236/ad0241531
OpenAlex: W1535810436
Cited by: 4,009
References: 0
Citations by year: 2026: 33 · 2025: 59 · 2024: 94 · 2023: 118 · 2022: 122 · 2021: 127 · 2020: 147 · 2019: 124 · 2018: 117 · 2017: 115 · 2016: 88 · 2015: 118 · 2014: 126 · 2013: 121 · 2012: 95
Type: report
Language: en
Open access: closed
Topics: Physical Sciences › Engineering › Electrical and Electronic Engineering › Analog and Mixed-Signal Circuit Design
Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
Physical Sciences › Engineering › Electrical and Electronic Engineering › VLSI and FPGA Design Techniques
Keywords: switching circuits, adaptive circuits

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