ADAPTIVE SWITCHING CIRCUITS

ARTIFICIAL NEURAL NETWORK AND ITS APPLICATIONS IN THE ENERGY SECTOR – AN OVERVIEW

Babatunde, D. E., Anozie, A. N., & Omoleye, J. A. (2020) · International Journal of Energy Economics and Policy · doi:10.32479/ijeep.8691 · Cited by 40

Acute stress enhances tolerance of uncertainty during decision-making

Byrne, K. A., Peters, C., Willis, H. C., Phan, D., Cornwall, A. C., & Worthy, D. A. (2020) · Cognition · doi:10.1016/j.cognition.2020.104448 · Cited by 41

Topological constraints and robustness in liquid state machines

Hazan, H., & Manevitz, L. M. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.06.052 · Cited by 42

Retrospective Revaluation as Simple Associative Learning.

Ghirlanda, S. (2005) · Journal of Experimental Psychology Animal Behavior Processes · doi:10.1037/0097-7403.31.1.107 · Cited by 38

Artificial neural network predictions of lengths of stay on a post-coronary care unit

Mobley, B. A., Leasure, R., & Davidson, L. (1995) · Heart & Lung · doi:10.1016/s0147-9563(05)80045-7 · Cited by 38

Pattern Recognition and Adaptive Control

Widrow, B. C. (1964) · IEEE Transactions on Applications and Industry · doi:10.1109/tai.1964.5407756 · Cited by 37

Representing and simulating strong ground motion

Jurkevics, A., & Ulrych, T. J. (1978) · Bulletin of the Seismological Society of America · doi:10.1785/bssa0680030781 · Cited by 37

Detection of tool breakage in milling operations—II. The neural network approach

Tansel, I., & McLaughlin, C. (1993) · International Journal of Machine Tools and Manufacture · doi:10.1016/0890-6955(93)90091-8 · Cited by 37

Autonomous processing in parallel distributed processing networks

Dawson, M. R. W., & Schopflocher, D. (1992) · Philosophical Psychology · doi:10.1080/09515089208573056 · Cited by 37

Inductive Model Generation for Text Classification Using a Bipartite Heterogeneous Network

Rossi, R. G., Lopes, A. D. A., Faleiros, T. D. P., & Rezende, S. O. (2014) · Journal of Computer Science and Technology · doi:10.1007/s11390-014-1436-7 · Cited by 36

Neural networks in two cascade algorithms for spectral reflectance reconstruction

Mansouri, A., Marzani, F. S., & Gouton, P. (2005) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2005.1530156 · Cited by 36

Spendthrift: Machine learning based resource and frequency scaling for ambient energy harvesting nonvolatile processors

Ma, K., Li, X., Srinivasa, S., Liu, Y., Sampson, J., Xie, Y., & Narayanan, V. (2017) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/aspdac.2017.7858402 · Cited by 38

Artificial Neural Networks

Shukla, A., Tiwari, R., & Kala, R. (2010) · Studies in computational intelligence · doi:10.1007/978-3-642-14344-1_2 · Cited by 39

Multivariable fuzzy control applied to the physical–chemical treatment facility of a Cellulose factory

Andújar, J. M., & Bravo, J. M. (2004) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2004.03.023 · Cited by 36

Analysis of Gradient Algorithms for TLS-Based Adaptive IIR Filters

Dunne, B. E., & Williamson, G. A. (2004) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2004.837408 · Cited by 36

Rainfall Estimation from Polarimetric S-Band Radar Measurements: Validation of a Neural Network Approach

Vulpiani, G., Giangrande, S., & Marzano, F. S. (2009) · Journal of Applied Meteorology and Climatology · doi:10.1175/2009jamc2172.1 · Cited by 37

Learning to Order BDD Variables in Verification

Grumberg, O., Livne, S., & Markovitch, S. (2003) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1096 · Cited by 36

Negative Transfer in Human Associative Learning

Griffiths, O., Johnson, A. M., & Mitchell, C. J. (2011) · Psychological Science · doi:10.1177/0956797611419305 · Cited by 41

Stability and Convergence Analysis of Transform-Domain LMS Adaptive Filters With Second-Order Autoregressive Process

Zhao, S., Man, Z., Khoo, S., & Wu, H. R. (2008) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2008.2007618 · Cited by 36

XCS with Computed Prediction for the Learning of Boolean Functions

Lanzi, P. L., Loiacono, D., Wilson, S. W., & Goldberg, D. E. (2005) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2005.1554736 · Cited by 36

Multi-feature adaptive classifiers for SAR image segmentation

Ceccarelli, M., & Petrosino, A. (1997) · Neurocomputing · doi:10.1016/s0925-2312(96)00038-0 · Cited by 36

Asymptotic Convergence of Backpropagation

Tesauro, G. J., He, Y. B., & Ahmad, S. (1989) · Neural Computation · doi:10.1162/neco.1989.1.3.382 · Cited by 36

Neural dynamic optimization for control systems.II. Theory

Seong, C.-Y., & Widrow, B. C. (2001) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/3477.938255 · Cited by 36

Motor unit recruitment in a distributed model of extraocular muscle

Dean, P. (1996) · Journal of Neurophysiology · doi:10.1152/jn.1996.76.2.727 · Cited by 36

Redundant arm kinematic control with recurrent loop

Lee, S., & Kil, R. M. (1994) · Neural Networks · doi:10.1016/0893-6080(94)90042-6 · Cited by 36

Leaning by Combining Memorization and Gradient Descent

Platt, J. C. (1990) · neural information processing systems · Cited by 36

Late-training amnesic deficits in probabilistic category learning: a neurocomputational analysis.Preprint

Gluck, M. A., Oliver, L. M., & Myers, C. E. (1996) · Learning & Memory · doi:10.1101/lm.3.4.326 · Cited by 36

Respiratory sound-base disease classification and characterization with deep/machine learning techniques

Karaarslan, O., Belcastro, K. D., & Ergen, O. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105570 · Cited by 37

Pavlovian-Instrumental Interaction in ‘Observing Behavior’

Beierholm, U., & Dayan, P. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000903 · Cited by 38

Rough set-based neuro-fuzzy system

Ang, K. K., & Quek, C. H. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2006.246758 · Cited by 35

Large Scale Online Multiple Kernel Regression with Application to Time-Series Prediction

Sahoo, D., Hoi, S. C. H., & Li, B. (2019) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3299875 · Cited by 37

Prediction and error in early infant speech learning: A speech acquisition model

Nixon, J. S., & Tomaschek, F. (2021) · Cognition · doi:10.1016/j.cognition.2021.104697 · Cited by 30

The power of social learning: How do observational and word-of-mouth learning influence online consumer decision processes?

Wang, F., Wang, M., Wan, Y., Jin, J. K., & Pan, Y. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102632 · Cited by 43

Research on the Feasibility of Applying GRU and Attention Mechanism Combined with Technical Indicators in Stock Trading Strategies

Lee, M.-C. (2022) · Applied Sciences · doi:10.3390/app12031007 · Cited by 36

Analysis of LMS algorithm with inputs from cyclostationary random processes

McLernon, D. (1991) · Electronics Letters · doi:10.1049/el:19910089 · Cited by 35

Improved variable step-size NLMS adaptive filtering algorithm for acoustic echo cancellation

Hamidia, M., & Amrouche, A. (2015) · Digital Signal Processing · doi:10.1016/j.dsp.2015.10.015 · Cited by 35

A NEURAL NETWORK BASED APPROACH TO MECHANICAL DESIGN OPTIMIZATION

Dhingra, A. K., & Rao, S. S. (1992) · Engineering Optimization · doi:10.1080/03052159208941280 · Cited by 36

An autoassociative model of causal reasoning and causal learning: Reply to Van Overwalle's (1998) critique of Read and Marcus-Newhall (1993).

Read, S. J., & Montoya, J. A. (1999) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.76.5.728 · Cited by 35

Adaptive signal processing for adaptive control

Widrow, B. C., & Walach, E. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1984.1172527 · Cited by 35

Applications of polynomial neural networks to FDIE and reconfigurable flight control

Barron, R. L., Cellucci, R. L., Jordan, P. R., Beam, N. E., Hess, P., & Barron, A. R. (2002) · IEEE Conference on Aerospace and Electronics · doi:10.1109/naecon.1990.112818 · Cited by 35

Classification of linearly nonseparable patterns by linear threshold elements

Roychowdhury, V., Siu, K.-Y. S., & Kailath, T. (1995) · IEEE Transactions on Neural Networks · doi:10.1109/72.363468 · Cited by 36

Optimal automatic periodic multiprocessor scheduler for fully specified flow graphs

Gelabert, P., & Barnwell, T. P., III (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.193223 · Cited by 35

Data Mining: Classification and Prediction

Urso, A. M., Fiannaca, A., Rosa, M. L., Ravì, V., & Rizzo, R. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.20461-5 · Cited by 38

Implementing probabilistic Neural Networks

Ancona, F., Colla, A. M., Stefano Rovetta, A., & Zunino, R. (1997) · Neural Computing and Applications · doi:10.1007/bf01413860 · Cited by 34

MIMYCS.Moisture, a process-based model of moisture content in developing maize kernels

Maiorano, A., Fanchini, D., & Donatelli, M. (2014) · European Journal of Agronomy · doi:10.1016/j.eja.2014.05.011 · Cited by 34

Estimation of river flow by artificial neural networks and identification of input vectors susceptible to producing unreliable flow estimates

Kentel, E. (2009) · Journal of Hydrology · doi:10.1016/j.jhydrol.2009.06.051 · Cited by 35

Theoretical XCS parameter settings of learning accurate classifiers

Nakata, M., Browne, W. N., Hamagami, T., & Takadama, K. (2017) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3071178.3071200 · Cited by 34

PCM-Trace: Scalable Synaptic Eligibility Traces with Resistivity Drift of Phase-Change Materials

Demirağ, Y., Moro, F., Dalgaty, T., Navarro, G., Frenkel, C., Indiveri, G., Vianello, E., & Payvand, M. (2021) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas51556.2021.9401446 · Cited by 35

XCS with computed prediction in multistep environments

Lanzi, P. L., Loiacono, D., Wilson, S. W., & Goldberg, D. E. (2005) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/1068009.1068323 · Cited by 34

Analysis of Acoustic Feedback/Echo Cancellation in Multiple-Microphone and Single-Loudspeaker Systems Using a Power Transfer Function Method

Guo, M., Elmedyb, T. B., Jensen, S. H., & Jensen, J. (2011) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2011.2168523 · Cited by 34

Toward a Noncommutative Arithmetic-geometric Mean Inequality: Conjectures, Case-studies, and Consequences

Recht, B., & Ré, C. (2012) · Cited by 34

Classification of motor commands using a modified self-organising feature map

Sebelius, F., Eriksson, L., Holmberg, H., Levinsson, A., Lundborg, G., Danielsen, N., Schouenborg, J., Balkenius, C., Laurell, T., & Montelius, L. (2005) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2004.09.008 · Cited by 34

The use of neural techniques in PIV and PTV

Grant, I., & Pan, X. (1997) · Measurement Science and Technology · doi:10.1088/0957-0233/8/12/004 · Cited by 35

A formal framework and extensions for function approximation in learning classifier systems

Drugowitsch, J., & Barry, A. M. (2007) · Machine Learning · doi:10.1007/s10994-007-5024-8 · Cited by 34

Evolution and generalization of a single neurone:

Raudys, Š. (1998) · Neural Networks · doi:10.1016/s0893-6080(97)00136-6 · Cited by 34

Wavelet packet based transient signal classification

Learned, R. E., Karl, W. C., & Willsky, A. S. (2003) · IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis · doi:10.1109/tftsa.1992.274223 · Cited by 34

An adaptive control for CARMA systems using linear neural networks

Watanabe, K., Fukuda, T., & Tzafestaş, S. G. (1992) · International Journal of Control · doi:10.1080/00207179208934324 · Cited by 34

Comments on "An adaptive recursive LMS filter"

Widrow, B. C., McCool, J., & Feintuch, P. (1977) · Proceedings of the IEEE · doi:10.1109/proc.1977.10723 · Cited by 34

Using a context-sensitive learning network for robot arm control

Yeung, D., & Bekey, G. A. (1989) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 34

Adaptive Estimation with Mutually Correlated Training Sequences

Daniell, T. (1970) · IEEE Transactions on Systems Science and Cybernetics · doi:10.1109/tssc.1970.300323 · Cited by 34

Computational Intelligence

Peters, J. F., & Witold Pedrycz, C. (1999) · Wiley eBooks · doi:10.1002/047134608x.w7101 · Cited by 35

Simplifying artificial neural network models of river basin behaviour by an automated procedure for input variable selection

de Oliveira, G. G., Pedrollo, O. C., & Castro, N. M. R. (2015) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2015.01.001 · Cited by 35

Early neural network development history: the age of Camelot

Eberhart, R. C., & Dobbins, R. W. (1990) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/51.59207 · Cited by 35

Modelling the Shear Behaviour of Clean Rock Discontinuities Using Artificial Neural Networks

Neto, S. A. D., Indraratna, B., Oliveira, D., & de Assis, A. P. (2017) · Rock Mechanics and Rock Engineering · doi:10.1007/s00603-017-1197-z · Cited by 35

Learning to Synchronize: Midfrontal Theta Dynamics during Rule Switching

Verbeke, P., Ergo, K., Loof, E. D., & Verguts, T. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.1874-20.2020 · Cited by 37

Bituminous Mixtures Experimental Data Modeling Using a Hyperparameters-Optimized Machine Learning Approach

Miani, M., Dunnhofer, M., Rondinella, F., Manthos, E., Valentin, J., Micheloni, C., & Baldo, N. (2021) · Applied Sciences · doi:10.3390/app112411710 · Cited by 36

Sensory motor remapping of space in human–machine interfaces

Mussa-Ivaldi, F. A., Casadio, M., Danziger, Z. C., Mosier, K. M., & Scheidt, R. A. (2011) · Progress in brain research · doi:10.1016/b978-0-444-53752-2.00014-x · Cited by 33

18 Financial applications of Artificial Neural Networks

Qi, M. (1996) · Handbook of statistics · doi:10.1016/s0169-7161(96)14020-7 · Cited by 33

The Hebbian-LMS Learning Algorithm

Widrow, B. C., Kim, Y., & Park, D. (2015) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2015.2471216 · Cited by 33

Multichannel active noise control system for local spectral reshaping of multifrequency noise

de Diego, M., González, A., Ferrer, M., & Piñero, G. (2003) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2003.05.009 · Cited by 33

A knowledge‐based service automation system for service logistics

Cheung, C. F., Chan, Y., Kwok, S. K.-S., Lee, W. B., & Wang, W. M. (2006) · Journal of Manufacturing Technology Management · doi:10.1108/17410380610678783 · Cited by 33

Statistical efficiency of adaptive algorithms

Widrow, B. C., & Kamenetsky, M. (2003) · Neural Networks · doi:10.1016/s0893-6080(03)00126-6 · Cited by 33

On Complexity Issues of Online Learning Algorithms

Yao, Y. (2010) · IEEE Transactions on Information Theory · doi:10.1109/tit.2010.2079010 · Cited by 33

A Novel Approach to Introducing Adaptive Filters Based on the LMS Algorithm and Its Variants

Soria, E., Calpe, J., Chambers, J. A., Martinez, M., Camps, G., & Martín‐Guerrero, J. D. (2004) · IEEE Transactions on Education · doi:10.1109/te.2003.822632 · Cited by 33

Anatomic segmentation improves prostate cancer detection with artificial neural networks analysis of1H magnetic resonance spectroscopic imaging

Matulewicz, Ł., Jansen, J. F. A., Bokacheva, L., Vargas, H. A., Akın, O., Fine, S. W., Shukla‐Dave, A., Eastham, J. A., Hricak, H., Koutcher, J. A., et al. (2013) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.24487 · Cited by 33

Classifier Systems for Continuous Payoff Environments

Wilson, S. W. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24855-2_96 · Cited by 33

Category dimensionality and feature knowledge: When more features are learned as easily as fewer.

Hoffman, A. B., & Murphy, G. L. (2006) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/0278-7393.32.3.301 · Cited by 33

Fuzzy techniques for adaptive nonlinear equalization

Patra, S. K., & Mulgrew, B. (2000) · Signal Processing · doi:10.1016/s0165-1684(00)00015-3 · Cited by 33

Introducing a genetic generalization pressure to the Anticipatory Classifier System - part 1: theoretical approach

Butz, M. V., Goldberg, D. E., & Stolzmann, W. (2000) · Cited by 33

Computational Aspects of Motor Control and Motor Learning

Jordan, M. I. (1996) · Cited by 34

Training neural networks with the GRG2 nonlinear optimizer

Hung, M. S., & Denton, J. W. (1993) · European Journal of Operational Research · doi:10.1016/0377-2217(93)90093-3 · Cited by 33

On the statistical efficiency of the LMS family of adaptive algorithms

Widrow, B. C., & Kamenetsky, M. (2004) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2003.1224027 · Cited by 33

Rapid learning with phase-change memory-based in-memory computing through learning-to-learn

Ortner, T., Petschenig, H., Vasilopoulos, A., Renner, R., Brglez, Š., Limbacher, T., Piñero-Fuentes, E., Linares-Barranco, A., Pantazi, A., & Legenstein, R. A. (2025) · Nature Communications · doi:10.1038/s41467-025-56345-4 · Cited by 35

From Actions to Goals and Vice-Versa: Theoretical Analysis and Models of the Ideomotor Principle and TOTE

Pezzulo, G., Baldassarre, G., Butz, M. V., Castelfranchi, C., & Hoffmann, J. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-74262-3_5 · Cited by 32

A personalized travel recommender system utilizing social network profile and accurate GPS data

Subramaniyaswamy, V., Vijayakumar, V., & Logesh, R. (2017) · Electronic Government an International Journal · doi:10.1504/eg.2018.10008842 · Cited by 32

Hybrid no-propagation learning for multilayer neural networks

Adhikari, S. P., Yang, C., Ślot, K., Strzelecki, M., & Kim, H. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.08.034 · Cited by 32

Artificial Neural Networks

Worden, K., Tsialiamanis, G. P., Cross, E. J., & Rogers, T. J. (2023) · Computational methods in engineering & the sciences · doi:10.1007/978-3-031-36644-4_2 · Cited by 36

Language, common sense, and the Winograd schema challenge

Browning, J., & LeCun, Y. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.104031 · Cited by 37

Techniques for Learning and Tuning Fuzzy Rule-Based Systems for Linguistic Modeling and their Application

Alcalá, R., Casillas, J., Cordón, Ó., Herrera, F., & Zwir, S. J. I. (2000) · Cited by 32

On surrogate methods in propeller optimisation

Vesting, F., & Bensow, R. (2014) · Ocean Engineering · doi:10.1016/j.oceaneng.2014.06.024 · Cited by 32

Cooperative Information Sharing to Improve Distributed Learning in Multi-Agent Systems

Dutta, P. S., Jennings, N. R., & Moreau, L. (2005) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1735 · Cited by 32

The stability of LMS

Solo, V. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650261 · Cited by 32

Certain Principles of Biomorphic Robots

Lewis, M. A., & Simó, L. S. (2001) · Autonomous Robots · doi:10.1023/a:1012430821608 · Cited by 32

State of the Art in Acoustic Echo Cancellation

Gilloire, A., Moulines, É., Slock, D. T. M., & Duhamel, P. (1996) · Springer eBooks · doi:10.1007/978-1-4471-1019-4_2 · Cited by 32

Stimulus configuration, spatial learning, and hippocampal function

Schmajuk, N. A., & Blair, H. T. (1993) · Behavioural Brain Research · doi:10.1016/0166-4328(93)90156-k · Cited by 32

Adaptive Ho-Kashyap rules for perceptron training

Hassoun, M. H., & Song, J. (1992) · IEEE Transactions on Neural Networks · doi:10.1109/72.105417 · Cited by 32

Charge-coupled device (CCD) adaptive discrete analog signal processing

White, M., Mack, I. A. G., Borsuk, G. M., Lampe, D. R., & Kub, F. J. (1979) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.1979.1051152 · Cited by 32

Robust learning approach for neuro-inspired nanoscale crossbar architecture

Chabi, D., Querlioz, D., Zhao, W. S., & Klein, J. (2014) · ACM Journal on Emerging Technologies in Computing Systems · doi:10.1145/2539123 · Cited by 31

From self-assessment to frustration, a small step toward autonomy in robotic navigation

Jauffret, A., Cuperlier, N., Tarroux, P., & Gaussier, P. (2013) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2013.00016 · Cited by 35

Development of a warning model for coastal freak wave occurrences using an artificial neural network

Doong, D.-J., Peng, J., & Chen, Y. (2018) · Ocean Engineering · doi:10.1016/j.oceaneng.2018.09.029 · Cited by 32

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