ADAPTIVE SWITCHING CIRCUITS

Distributed ARTMAP: a neural network for fast distributed supervised learning

Carpenter, G. A., Milenova, B. L., & Noeske, B. W. (1998) · Neural Networks · doi:10.1016/s0893-6080(98)00019-7 · Cited by 151

Characteristics of randomly connected threshold-element networks and network systems

Амари, Ш.-И. (1971) · Proceedings of the IEEE · doi:10.1109/proc.1971.8087 · Cited by 152

Learning Process in an Asymmetric Threshold Network

LeCun, Y. (1986) · Springer eBooks · doi:10.1007/978-3-642-82657-3_24 · Cited by 170

Worst-case quadratic loss bounds for prediction using linear functions and gradient descent

Cesa‐Bianchi, N., Long, P. M., & Warmuth, M. K. (1996) · IEEE Transactions on Neural Networks · doi:10.1109/72.501719 · Cited by 148

Artificial neural networks for four-coloring map problems and K-colorability problems

Takefuji, Y., & Lee, K. C. (1991) · IEEE Transactions on Circuits and Systems · doi:10.1109/31.101328 · Cited by 145

Linear Decision Functions, with Application to Pattern Recognition

Highleyman, W. H. (1962) · Proceedings of the IRE · doi:10.1109/jrproc.1962.288194 · Cited by 147

Distributed Asynchronous Incremental Subgradient Methods

Nedić, A., Bertsekas, D. P., & Borkar, V. S. (2001) · Studies in computational mathematics · doi:10.1016/s1570-579x(01)80023-9 · Cited by 146

PVLV: The Primary Value and Learned Value Pavlovian Learning Algorithm.

O’Reilly, R. C., Frank, M. J., Hazy, T. E., & Watz, B. (2007) · Behavioral Neuroscience · doi:10.1037/0735-7044.121.1.31 · Cited by 147

Theoretical and computational analysis of skill learning, repetition priming, and procedural memory.

Gupta, P., & Cohen, N. J. (2002) · Psychological Review · doi:10.1037/0033-295x.109.2.401 · Cited by 150

Introduction to neural networks for intelligent control

Bavarian, B. (1988) · IEEE Control Systems Magazine · doi:10.1109/37.1866 · Cited by 145

A neural network model of the structure and dynamics of human personality.

Read, S. J., Monroe, B. M., Brownstein, A. L., Yang, Y., Chopra, G., & Miller, L. C. (2010) · Psychological Review · doi:10.1037/a0018131 · Cited by 257

Adaptive filtering with binary reinforcement

Gersho, A. (1984) · IEEE Transactions on Information Theory · doi:10.1109/tit.1984.1056890 · Cited by 144

Deep neural networks with weighted spikes

Kim, J., Kim, H., Huh, S., Lee, J., & Choi, K. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.087 · Cited by 155

Performance analysis of general tracking algorithms

Guo, L., & Lennart Ljung, A. (1995) · IEEE Transactions on Automatic Control · doi:10.1109/9.402230 · Cited by 142

Learning and Maintaining Saccadic Accuracy: A Model of Brainstem–Cerebellar Interactions

Dean, P., Mayhew, J. E. W., & Langdon, P. (1994) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1994.6.2.117 · Cited by 142

The merging of neural networks, fuzzy logic, and genetic algorithms

Shapiro, A. F. (2002) · Insurance Mathematics and Economics · doi:10.1016/s0167-6687(02)00124-5 · Cited by 143

Learning styles' recognition in e‐learning environments with feed‐forward neural networks

Villaverde, J. E., Godoy, D. L., & Amandi, A. (2006) · Journal of Computer Assisted Learning · doi:10.1111/j.1365-2729.2006.00169.x · Cited by 144

On the Convergence of the LMS Algorithm with Adaptive Learning Rate for Linear Feedforward Networks

Luo, Z.-Q. T. (1991) · Neural Computation · doi:10.1162/neco.1991.3.2.226 · Cited by 140

Predictive and diagnostic learning within causal models: Asymmetries in cue competition.

Waldmann, M. R., & Holyoak, K. J. (1992) · Journal of Experimental Psychology General · doi:10.1037//0096-3445.121.2.222 · Cited by 402

On-line regression algorithms for learning mechanical models of robots: A survey

Sigaud, O., Salaün, C., & Padois, V. (2011) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2011.07.006 · Cited by 144

The Role of the Anterior Cingulate Cortex in Prediction Error and Signaling Surprise

Alexander, W. H., & Brown, J. W. (2017) · Topics in Cognitive Science · doi:10.1111/tops.12307 · Cited by 147

An automatic system of classification of weld defects in radiographic images

Vilar, R., Zapata, J., & Ruı́z, R. (2009) · NDT & E International · doi:10.1016/j.ndteint.2009.02.004 · Cited by 136

Non-linear regression methods in NIRS quantitative analysis

Pérez‐Marín, D., Garrido‐Varo, A., & GUERRERO, J. (2006) · Talanta · doi:10.1016/j.talanta.2006.10.036 · Cited by 136

Neural network architectures for robotic applications

King, S. Y., & Hwang, J. (1989) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.88082 · Cited by 134

Data-Aided Versus Blind Single-Carrier Coherent Receivers

Kuschnerov, M., Chouayakh, M., Piyawanno, K., Spinnler, B., Man, E. D., Kainzmaier, P., Alfiad, M. S., Napoli, A., & Lankl, B. (2010) · IEEE photonics journal · doi:10.1109/jphot.2010.2048308 · Cited by 134

The Robustness of the p-Norm Algorithms

Gentile, C. (2003) · Machine Learning · doi:10.1023/a:1026319107706 · Cited by 134

An improved method for developing neural networks: The case of evaluating commercial loan creditworthiness

Glorfeld, L. W., & Hardgrave, B. C. (1996) · Computers & Operations Research · doi:10.1016/0305-0548(96)00009-3 · Cited by 131

Learning to divide the labor: an account of deficits in light and heavy verb production

Gordon, J., & Dell, G. S. (2003) · Cognitive Science · doi:10.1016/s0364-0213(02)00111-8 · Cited by 130

Dual-microphone active noise cancellation paved with Doppler assimilation for TADS

Liu, F., Zhao, X., Zhu, Z., Zhai, Z., & Liu, Y. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.109727 · Cited by 131

Predictive modelling of coniferous forest age using statistical and artificial neural network approaches applied to remote sensor data

J. R. JENSEN, F. Q. (1999) · International Journal of Remote Sensing · doi:10.1080/014311699211804 · Cited by 131

Dynamic recurrent neural networks

Pearlmutter, B. A. (2018) · Maynooth University ePrints and eTheses Archive (Maynooth University) · doi:10.1184/r1/6605018 · Cited by 131

Back-propagation learning in expert networks

Lacher, R. C., Hruska, S. I., & Kuncicky, D. C. (1992) · IEEE Transactions on Neural Networks · doi:10.1109/72.105418 · Cited by 129

Learning in Big Data: Introduction to Machine Learning

Bouchefry, K. E., & de Souza, R. S. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819154-5.00023-0 · Cited by 149

Bioelectronic noses: a status report. Part II1Co-ordinating authors: Ch. Ziegler and W. Göpel. All other authors are contributing authors.1

Ziegler, C., Göpel†, W., Hämmerle, H., Hatt, H. H., Jung, G., Laxhuber, L. A., Schmidt, H., Schütz, S., Vögtle, F., & Zell, A. (1998) · Biosensors and Bioelectronics · doi:10.1016/s0956-5663(97)00093-6 · Cited by 130

Performance of adaptive estimation algorithms in dependent random environments

Bitmead, R. R., & Anderson, B. D. O. (1980) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1980.1102433 · Cited by 128

Nanowire FET Based Neural Element for Robotic Tactile Sensing Skin

Taube Navaraj, W., García Núñez, C., Shakthivel, D., Vinciguerra, V., Labeau, F., Gregory, D. H., & Dahiya, R. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00501 · Cited by 127

A Brief History of Connectionism

Medler, D. A. (1998) · Cited by 126

Application of back-propagation networks in debris flow prediction

Chang, T.-C., & Chao, R. (2006) · Engineering Geology · doi:10.1016/j.enggeo.2006.02.007 · Cited by 127

A tree-structured adaptive network for function approximation in high-dimensional spaces

Sanger, T. D. (1991) · IEEE Transactions on Neural Networks · doi:10.1109/72.80339 · Cited by 127

Memristor-Based Echo State Network With Online Least Mean Square

Wen, S., Hu, R., Yang, Y., Huang, T., Zeng, Z., & Song, Y. (2018) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2018.2825021 · Cited by 126

Occasion setting: A neural network approach.

Schmajuk, N. A., Lamoureux, J. A., & Holland, P. C. (1998) · Psychological Review · doi:10.1037/0033-295x.105.1.3 · Cited by 125

A Novel Data-Driven Model for Real-Time Influenza Forecasting

Venna, S. R., Tavanaei, A., Gottumukkala, R. N., Raghavan, V. V., Maida, A. S., & Nichols, S. (2018) · IEEE Access · doi:10.1109/access.2018.2888585 · Cited by 127

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

Uwaechia, A. N., & Ramli, D. A. (2021) · IEEE Access · doi:10.1109/access.2021.3095248 · Cited by 136

Least squares type algorithm for adaptive implementation of Pisarenko's harmonic retrieval method

Reddy, V. U., Egardt, B. S., & Kailath, T. (1982) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1982.1163905 · Cited by 123

Introduction to computation and learning in artificial neural networks

Másson, E., & Wang, Y.-J. (1990) · European Journal of Operational Research · doi:10.1016/0377-2217(90)90085-p · Cited by 124

Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks

Soltoggio, A., Stanley, K. O., & Risi, S. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.07.013 · Cited by 132

An algorithm to generate radial basis function (RBF)-like nets for classification problems

Roy, A., Govil, S., & Miranda, R. (1995) · Neural Networks · doi:10.1016/0893-6080(94)00064-s · Cited by 121

Feedforward Artificial Neural Networks for Solving Discrete Multiple Criteria Decision Making Problems

Malakooti, B. B., & Zhou, Y. Q. (1994) · Management Science · doi:10.1287/mnsc.40.11.1542 · Cited by 120

Function Approximation With XCS: Hyperellipsoidal Conditions, Recursive Least Squares, and Compaction

Butz, M. V., Lanzi, P. L., & Wilson, S. W. (2008) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2007.903551 · Cited by 120

Switching between internal and external modes: A multiscale learning principle

Honey, C. J., Newman, E. L., & Schapiro, A. C. (2017) · Network Neuroscience · doi:10.1162/netn_a_00024 · Cited by 115

A study of tool life in hot machining using artificial neural networks and regression analysis method

Tosun, N., & Özler, L. (2002) · Journal of Materials Processing Technology · doi:10.1016/s0924-0136(02)00086-9 · Cited by 120

Neural networks for automatic target recognition

Rogers, S. K., Colombi, J. M., Martin, C. E., Gainey, J. C., Fielding, K. H., Burns, T. J., Ruck, D. W., Kabrisky, M., & Oxley, M. E. (1995) · Neural Networks · doi:10.1016/0893-6080(95)00050-x · Cited by 119

The use of an adaptive threshold element to design a linear optimal pattern classifier

Koford, J. S., & Groner, G. F. (1966) · IEEE Transactions on Information Theory · doi:10.1109/tit.1966.1053856 · Cited by 119

Learning and Inductive Inference

(1982) · Elsevier eBooks · doi:10.1016/b978-0-86576-091-2.50009-5 · Cited by 119

Efficient Processing of Deep Neural Networks

Sze, V., Chen, Y., Yang, T.-J., & Emer, J. (2020) · Synthesis lectures on computer architecture · doi:10.2200/s01004ed1v01y202004cac050 · Cited by 125

Artificial Neural Networks for Decision-Making in Urologic Oncology

Anagnostou, T., Mesut Remzi, A., Lykourinas, M., & Djavan, B. (2003) · European Urology · doi:10.1016/s0302-2838(03)00133-7 · Cited by 118

Decision trees can initialize radial-basis function networks

Kubát, M. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/72.712154 · Cited by 118

Stable task information from an unstable neural population

Rule, M. E., Loback, A. R., Raman, D. V., Driscoll, L. N., Harvey, C. D., & O'Leary, T. (2020) · eLife · doi:10.7554/elife.51121 · Cited by 106

Establishing a rainfall threshold for flash flood warnings in China’s mountainous areas based on a distributed hydrological model

Miao, Q., Yang, D., Yang, H., & Li, Z. (2016) · Journal of Hydrology · doi:10.1016/j.jhydrol.2016.04.054 · Cited by 117

The selection of weight accuracies for Madalines

Piche, S. (1995) · IEEE Transactions on Neural Networks · doi:10.1109/72.363478 · Cited by 117

Hierarchical Error Representation: A Computational Model of Anterior Cingulate and Dorsolateral Prefrontal Cortex

Alexander, W. H., & Brown, J. W. (2015) · Neural Computation · doi:10.1162/neco_a_00779 · Cited by 119

PLS/neural networks

Holcomb, T. R., & Morari, M. (1992) · Computers & Chemical Engineering · doi:10.1016/0098-1354(92)80056-f · Cited by 114

Stimulus Generalization and Representation in Adaptive Network Models of Category Learning

Gluck, M. A. (1991) · Psychological Science · doi:10.1111/j.1467-9280.1991.tb00096.x · Cited by 116

Analog electronic neural network circuits

Graf, H. P., & Jackel, L. D. (1989) · IEEE Circuits and Devices Magazine · doi:10.1109/101.29902 · Cited by 114

Adaptive Lattice Decision-Feedback Equalizers--Their Performance and Application to Time-Variant Multipath Channels

Ling, F., & Proakis, J. G. (1985) · IEEE Transactions on Communications · doi:10.1109/tcom.1985.1096300 · Cited by 115

Prévision hydrologique par réseaux de neurones artificiels : état de l'art

Coulibaly, P., Anctil, F., & Bobée, B. (1999) · Canadian Journal of Civil Engineering · doi:10.1139/cjce-26-3-293 · Cited by 119

A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications

James, C. D., Aimone, J. B., Miner, N. E., Vineyard, C. M., Rothganger, F. H., Carlson, K. D., Mulder, S. A., Draelos, T. J., Faust, A., Marinella, M. J., et al. (2017) · Biologically Inspired Cognitive Architectures · doi:10.1016/j.bica.2016.11.002 · Cited by 115

Neural Networks for System Identification

Chu, R., Shoureshi, R. A., & Tenorio, M. F. (1989) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc.1989.4790321 · Cited by 113

Neural Networks For Data Mining

Zhang, G. P. (2009) · Springer eBooks · doi:10.1007/978-0-387-09823-4_21 · Cited by 113

Asymptotic and finite-sample properties of estimators based on stochastic gradients

Toulis, P., & Airoldi, E. M. (2017) · The Annals of Statistics · doi:10.1214/16-aos1506 · Cited by 123

Neural network learning control of robot manipulators using gradually increasing task difficulty

Sanger, T. D. (1994) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.294207 · Cited by 111

Evolving space-filling curves to distribute radial basis functions over an input space

Whitehead, B. A., & Choate, T. D. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.265957 · Cited by 110

An adaptive estimation of periodic signals using a Fourier linear combiner

Vaz, C., Kong, X., & Thakor, N. (1994) · IEEE Transactions on Signal Processing · doi:10.1109/78.258116 · Cited by 108

Application of artificial neural network and genetic algorithm in flow and transport simulations

Morshed, J., & Kaluarachchi, J. J. (1998) · Advances in Water Resources · doi:10.1016/s0309-1708(98)00002-5 · Cited by 111

Performance analysis of LMS adaptive prediction filters

Zeidler, J. R. (1990) · Proceedings of the IEEE · doi:10.1109/5.60921 · Cited by 109

Foundations of Machine Learning

Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018) · doi:10.7551/mitpress/10290.001.0001 · Cited by 1,122

Learning rules in spiking neural networks: A survey

Yi, Z., Lian, J., Liu, Q., Zhu, H., Liang, D., & Liu, J. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.02.026 · Cited by 112

Conjugate-gradient neural networks in classification of multisource and very-high-dimensional remote sensing data

Benediktsson, J. A., Swain, P. H., & Ersoy, O. K. (1993) · International Journal of Remote Sensing · doi:10.1080/01431169308904316 · Cited by 108

New Algorithms for Improved Adaptive Convex Combination of LMS Transversal Filters

Arenas‐García, J., Gómez-Verdejo, V., & Figueiras-Vidal, A. R. (2005) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2005.858823 · Cited by 108

Digital adaptive filters: Conditions for convergence, rates of convergence, effects of noise and errors arising from the implementation

Weiss, A. J., & Mitra, D. (1979) · IEEE Transactions on Information Theory · doi:10.1109/tit.1979.1056103 · Cited by 108

Biologically Inspired Modular Neural Networks

Azam, F. (2000) · Cited by 108

Simulation of anticipatory responses in classical conditioning by a neuron-like adaptive element

Barto, A. G., & Sutton, R. S. (1982) · Behavioural Brain Research · doi:10.1016/0166-4328(82)90001-8 · Cited by 106

Higher-ordered and intraconnected bidirectional associative memories

Simpson, P. K. (1990) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.57276 · Cited by 106

An adaptive neural network approach to one-week ahead load forecasting

Peng, T.-M., Hubele, N. F., & Karady, G. G. (1993) · IEEE Transactions on Power Systems · doi:10.1109/59.260877 · Cited by 106

Artificial neural networks for pattern recognition

Yegnanarayana, B. (1994) · Sadhana · doi:10.1007/bf02811896 · Cited by 111

Uncertainty and Learning

Dayan, P., & Jyu, A. (2003) · IETE Journal of Research · doi:10.1080/03772063.2003.11416335 · Cited by 105

Ranking and Reranking with Perceptron

Shen, L., & Joshi, A. K. (2005) · Machine Learning · doi:10.1007/s10994-005-0918-9 · Cited by 106

Sparse Adaptive Filters for Echo Cancellation

Paleologu, C., Benesty, J., & Ciochină, S. (2010) · Synthesis lectures on speech and audio processing · doi:10.2200/s00289ed1v01y201006sap006 · Cited by 105

Neural Networks and Animal Behavior

Enquist, M., & Ghirlanda, S. (2013) · Princeton University Press eBooks · doi:10.1515/9781400850785 · Cited by 105

Review and reappraisal of adaptive interfaces: Toward biologically inspired paradigms

Rothrock, L., Koubek, R. J., Fuchs, F., Haas, M., & Salvendy, G. (2002) · Theoretical Issues in Ergonomics Science · doi:10.1080/14639220110110342 · Cited by 112

Adaptive control of an active seat for occupant vibration reduction

Gan, Z., Hillis, A. J., & Darling, J. (2015) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2015.03.050 · Cited by 104

Neural Computations Mediating One-Shot Learning in the Human Brain

Lee, S. W., O’Doherty, J. P., & Shimojo, S. (2015) · PLoS Biology · doi:10.1371/journal.pbio.1002137 · Cited by 111

Hybrid feedback PID-FxLMS algorithm for active vibration control of cantilever beam with piezoelectric stack actuator

Li, W., Yang, Z., Li, K., & Wang, W. (2021) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2021.116243 · Cited by 104

An Integrated LMS Adaptive Filter of TX Leakage for CDMA Receiver Front Ends

Aparin, V., Ballantyne, G. J., Persico, C., & Cicalini, A. (2006) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2006.872738 · Cited by 103

Cognitive Dynamic Systems

Haykin, S. (2006) · Proceedings of the IEEE · doi:10.1109/jproc.2006.886014 · Cited by 103

Methods and Procedures for the Verification and Validation of Artificial Neural Networks

Taylor, B. J., Taylor, B. J., Brian, E., & Taylor, J. (2006) · Kluwer Academic Publishers eBooks · doi:10.1007/0-387-29485-6 · Cited by 105

An Optimality Principle for Unsupervised Learning

Sanger, T. D. (1988) · Cited by 104

Self-Orthogonalizing Adaptive Equalization Algorithms

Gitlin, R. D., & Magee, F. R., Jr. (1977) · IRE Transactions on Communications Systems · doi:10.1109/tcom.1977.1093886 · Cited by 103

System-Level Design Considerations for Digital Pre-Distortion of Wireless Base Station Transmitters

Wood, J. (2017) · IEEE Transactions on Microwave Theory and Techniques · doi:10.1109/tmtt.2017.2659738 · Cited by 102

Robust and sparsity-aware adaptive filters: A Review

Kumar, K., Pandey, R., Karthik, M., Bhattacharjee, S. S., & George, N. V. (2021) · Signal Processing · doi:10.1016/j.sigpro.2021.108276 · Cited by 102

Theory & design of switching circuits

Friedman, A. D., & Menon, P. R. (1975) · Cited by 13

Synthesis of Switching Circuits

Klir, G. J., & Seidl, L. K. (1968) · Cited by 1

A new approach to The design of switching circuits

Curtis, H. A. (1962) · Cited by 222

Electronic Switching Theory and Circuits

Beuscher, H. J. (1971) · Cited by 2

Adaptive separation in ternary switching circuits

Moraga, C. (1977) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)66233-5 · Cited by 1