Optimization and applications of echo state networks with leaky- integrator neurons

The Elements of Statistical Learning

Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162

A tutorial on hidden Markov models and selected applications in speech recognition

Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743

On combining classifiers

Kittler, J., Hatef, M., Duin, R. P. W., & Matas, J. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.667881 · Cited by 5,394

Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations

Maass, W., Natschläger, T., & Markram, H. (2002) · Neural Computation · doi:10.1162/089976602760407955 · Cited by 4,025

Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication

Jaeger, H., & Haas, H. (2004) · Science · doi:10.1126/science.1091277 · Cited by 3,811

Minimum prediction residual principle applied to speech recognition

Itakura, F. (1975) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1975.1162641 · Cited by 1,620

The neural basis of temporal processing

Mauk, M. D., & Buonomano, D. V. (2004) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.27.070203.144247 · Cited by 976

Adaptive Filters Theory and Applications

Farhang‐Boroujeny, B. (1999) · doi:10.1002/9781118591352 · Cited by 965

Gradient calculations for dynamic recurrent neural networks: a survey

Pearlmutter, B. A. (1995) · IEEE Transactions on Neural Networks · doi:10.1109/72.410363 · Cited by 578

Readings in Speech Recognition

(1990) · Elsevier eBooks · doi:10.1016/c2009-0-27652-5 · Cited by 574

Memory in linear recurrent neural networks in continuous time

Hermans, M., & Schrauwen, B. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.08.008 · Cited by 75

Evolutionary aspects of reservoir computing

Seoane, L. F. (2019) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2018.0377 · Cited by 74

An Echo State Network for fuel cell lifetime prediction under a dynamic micro-cogeneration load profile

Mezzi, R., Steiner, N. Y., Péra, M., Hissel, D., & Larger, L. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.116297 · Cited by 74

Stable Output Feedback in Reservoir Computing Using Ridge Regression

wyffels, F., Schrauwen, B., & Stroobandt, D. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87536-9_83 · Cited by 73

A novel photovoltaic power forecasting model based on echo state network

Yao, X., Wang, Z., & Zhang, H. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.10.022 · Cited by 72

Dynamic Flux Tubes Form Reservoirs of Stability in Neuronal Circuits

Monteforte, M., & Wolf, F. (2012) · Physical Review X · doi:10.1103/physrevx.2.041007 · Cited by 76

Game Theory for Next Generation Wireless and Communication Networks

Han, Z., Niyato, D. T., Saad, W., & Başar, T. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108277402 · Cited by 82

Acoustic Modeling With Hierarchical Reservoirs

Triefenbach, F., Jalalvand, A., Demuynck, K., & Martens, J. (2013) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2013.2280209 · Cited by 69

Deep Echo State Network (DeepESN): A Brief SurveyPreprint

Gallicchio, C., & Micheli, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.04323 · Cited by 91

Feedback Control by Online Learning an Inverse Model

Waegeman, T., wyffels, F., & Schrauwen, B. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2208655 · Cited by 68

Echo State Networks-Based Reservoir Computing for MNIST Handwritten Digits Recognition

Schaetti, N., Salomon, M., & Couturier, R. (2016) · IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) · doi:10.1109/cse-euc-dcabes.2016.229 · Cited by 77

Local Lyapunov exponents of deep echo state networks

Gallicchio, C., Micheli, A., & Silvestri, L. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.11.073 · Cited by 66

A novel model of leaky integrator echo state network for time-series prediction

Lun, S., Yao, X., Qi, H.-Y., & Hu, H. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.02.029 · Cited by 65

Reservoir computing with diverse timescales for prediction of multiscale dynamics

Tanaka, G., MATSUMORI, T., Yoshida, H., & Aihara, K. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.l032014 · Cited by 73

Data-driven based model for flow prediction of steam system in steel industry

Liu, Y., Liu, Q., Wei, W., Zhao, J., & Leung, H. (2012) · Information Sciences · doi:10.1016/j.ins.2011.12.031 · Cited by 63

Convolutional Multitimescale Echo State Network

Ma, Q., Chen, E., Lin, Z., Yan, J., Yu, Z., & Ng, W. W. Y. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2919648 · Cited by 60

A local Echo State Property through the largest Lyapunov exponent

Wainrib, G., & Galtier, M. N. (2016) · Neural Networks · doi:10.1016/j.neunet.2015.12.013 · Cited by 64

A new echo state network with variable memory length

Lun, S., Yao, X., & Hu, H. (2016) · Information Sciences · doi:10.1016/j.ins.2016.07.065 · Cited by 60

Effects of connectivity structure of complex echo state network on its prediction performance for nonlinear time series

Song, Q., & Feng, Z. (2010) · Neurocomputing · doi:10.1016/j.neucom.2010.01.015 · Cited by 60

Delay-dependent stability analysis of the QUAD vector field fractional order quaternion-valued memristive uncertain neutral type leaky integrator echo state neural networks

Pahnehkolaei, S. M. A., Alfi, A., & Machado, J. A. T. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.05.015 · Cited by 59

Quantized Minimum Error Entropy Criterion

Chen, B., Xing, L., Zheng, N., & Príncipe, J. C. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2868812 · Cited by 61

An extreme learning machine-based method for computational PDEs in higher dimensions

Wang, Y., & Dong, S. S. (2023) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2023.116578 · Cited by 66

A Dynamically Stabilized Recurrent Neural Network

Saab, S., Fu, Y., Ray, A., & Hauser, M. (2021) · Neural Processing Letters · doi:10.1007/s11063-021-10676-7 · Cited by 58

Real-time personal healthcare data analysis using edge computing for multimodal wearable sensors

Matsumura, G., Honda, S., Kikuchi, T., Mizuno, Y., Hara, H., Kondo, Y., Nakamura, H., Watanabe, S., Hayakawa, K., Nakajima, K., et al. (2024) · Device · doi:10.1016/j.device.2024.100597 · Cited by 60

Dynamical Learning of Dynamics

Klos, C., Kossio, Y. F. K., Goedeke, S., Gilra, A., & Memmesheimer, R.-M. (2020) · Physical Review Letters · doi:10.1103/physrevlett.125.088103 · Cited by 55

Echo State Networks for Practical Nonlinear Model Predictive Control of Unknown Dynamic Systems

Jordanou, J. P., Antonelo, E. A., & Camponogara, E. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3136357 · Cited by 59

Balanced echo state networks

Koryakin, D., Lohmann, J., & Butz, M. V. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.08.008 · Cited by 55

Reservoir Computing as a Model for In-Materio Computing

Dale, M., Miller, J. F., & Stepney, S. (2016) · Emergence, complexity and computation · doi:10.1007/978-3-319-33924-5_22 · Cited by 55

Model Predictive Control Design for Dynamical Systems Learned by Echo State Networks

Armenio, L. B., Terzi, E., Farina, M., & Scattolini, R. (2019) · IEEE Control Systems Letters · doi:10.1109/lcsys.2019.2920720 · Cited by 55

From birdsong to human speech recognition: Bayesian inference on a hierarchy of nonlinear dynamical systems

Yildiz, I. B., von Kriegstein, K., & Kiebel, S. J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003219 · Cited by 55

Estimating the non-linear dynamics of free-flying objects

Kim, S., & Billard, A. (2012) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2012.05.022 · Cited by 55

Multi-reservoir echo state networks with sequence resampling for nonlinear time-series prediction

Li, Z., & Tanaka, G. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.122 · Cited by 55

A survey of deep learning techniques based Parkinson’s disease recognition methods employing clinical data

Haq, A. U., Li, J., Agbley, B. L. Y., Mawuli, C. B., Ali, Z., Nazir, S., & Din, S. U. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118045 · Cited by 63

A novel compound fault-tolerant method based on online sequential extreme learning machine with cycle reservoir for turbofan engine direct thrust control

Zhou, X., Huang, J., Lü, F., Zhou, W., & Liu, P. (2022) · Aerospace Science and Technology · doi:10.1016/j.ast.2022.108059 · Cited by 53

The architecture of dynamic reservoir in the echo state network

Cui, H., Liu, X., & Li, L. (2012) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4746765 · Cited by 53

Fluid network dynamics in the prefrontal cortex during multiple strategy switching

Malagon‐Vina, H., Ciocchi, S., Passecker, J., Dorffner, G., & Klausberger, T. (2018) · Nature Communications · doi:10.1038/s41467-017-02764-x · Cited by 58

Applications of Metaheuristics in Reservoir Computing Techniques: A Review

Bala, A., Ismail, I., Ibrahim, R., & Sait, S. M. (2018) · IEEE Access · doi:10.1109/access.2018.2873770 · Cited by 53

A White-Box Equivalent Neural Network Circuit Model for SoC Estimation of Electrochemical Cells

Luzi, M., Mascioli, F. M. F., Paschero, M., & Rizzi, A. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2901062 · Cited by 53

Output-Tracking-Error-Constrained Robust Positioning Control for a Nonsmooth Nonlinear Dynamic System

Han, S.-I., & Lee, J. M. (2014) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2316263 · Cited by 52

Real-time detection of epileptic seizures in animal models using reservoir computing

Buteneers, P., Verstraeten, D., Nieuwenhuyse, B. V., Stroobandt, D., Raedt, R., Vonck, K. E. J., Boon, P., & Schrauwen, B. (2012) · Epilepsy Research · doi:10.1016/j.eplepsyres.2012.07.013 · Cited by 52

Maximum Information Exploitation Using Broad Learning System for Large-Scale Chaotic Time-Series Prediction

Han, M., Li, W., Feng, S., Qiu, T., & Chen, C. L. P. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3004253 · Cited by 52

Echo spiking neural P systems

Long, L., Lugu, R., Xiong, X., Liu, Q., Peng, H. J., Wang, J., Orellana-Martín, D., & Pérez-Jímenez, M. J. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109568 · Cited by 51

Broad echo state network for multivariate time series prediction

Yao, X., & Wang, Z. (2019) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2019.01.027 · Cited by 52

Learning-Based Optimal Cooperative Formation Tracking Control for Multiple UAVs: A Feedforward-Feedback Design Framework

Zhang, B., Lv, M., Cui, S., Bu, X., & Park, J. H. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3322028 · Cited by 50

Stochastic nonlinear time series forecasting using time-delay reservoir computers: Performance and universality

Grigoryeva, L., Henriques, J., Larger, L., & Ortega, J. (2014) · Neural Networks · doi:10.1016/j.neunet.2014.03.004 · Cited by 46

Optimal nonlinear information processing capacity in delay-based reservoir computers

Grigoryeva, L., Henriques, J., Larger, L., & Ortega, J. (2015) · Scientific Reports · doi:10.1038/srep12858 · Cited by 48

Interpreting Recurrent Neural Networks Behaviour via Excitable Network Attractors

Ceni, A., Ashwin, P., & Livi, L. (2019) · Cognitive Computation · doi:10.1007/s12559-019-09634-2 · Cited by 51

In-Ear EEG Based Attention State Classification Using Echo State Network

Jeong, D., & Jeong, J. (2020) · Brain Sciences · doi:10.3390/brainsci10060321 · Cited by 51

Dynamic stability analysis of fractional order leaky integrator echo state neural networks

Pahnehkolaei, S. M. A., Alfi, A., & Machado, J. A. T. (2016) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2016.11.013 · Cited by 48

Parameterizing echo state networks for multi-step time series prediction

Viehweg, J., Worthmann, K., & Mäder, P. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.11.044 · Cited by 51

Selection of cortical dynamics for motor behaviour by the basal ganglia

Mannella, F., & Baldassarre, G. (2015) · Biological Cybernetics · doi:10.1007/s00422-015-0662-6 · Cited by 50

Combining forward with recurrent neural networks for hourly air quality prediction in Northwest of China

Zhao, Z., Qin, J., He, Z., Li, H., Yang, Y., & Zhang, R. (2020) · Environmental Science and Pollution Research · doi:10.1007/s11356-020-08948-1 · Cited by 46

Prediction and identification of discrete-time dynamic nonlinear systems based on adaptive echo state network

Yao, X., Wang, Z., & Zhang, H. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.01.003 · Cited by 47

Deep Echo State Network With Multiple Adaptive Reservoirs for Time Series Prediction

Wang, Z., Yao, X., Huang, Z., & Liu, L. (2021) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2021.3062177 · Cited by 47

A Review of Designs and Applications of Echo State NetworksPreprint

Sun, C., Song, M., Hong, S., & Li, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.02974 · Cited by 61

Position-Based Visual Servo Control of Dual Robotic Arms With Unknown Kinematic Models: A Cerebellum- Inspired Approach

Yu, P., Tan, N., & Mao, M. (2023) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2023.3235902 · Cited by 46

Hierarchical delay-memory echo state network: A model designed for multi-step chaotic time series prediction

Na, X., Ren, W., & Xu, X. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104229 · Cited by 46

Generative Modeling of Autonomous Robots and their Environments using Reservoir Computing

Antonelo, E. A., Schrauwen, B., & Campenhout, J. V. (2007) · Neural Processing Letters · doi:10.1007/s11063-007-9054-9 · Cited by 45

Reservoir computing models based on spiking neural P systems for time series classification

Peng, H. J., Xiong, X., Wu, M., Wang, J., Yang, Q., Orellana-Martín, D., & Pérez-Jímenez, M. J. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.10.041 · Cited by 45

Uniform stability of Fractional Order Leaky Integrator Echo State Neural Network with multiple time delays

Pahnehkolaei, S. M. A., Alfi, A., & Machado, J. A. T. (2017) · Information Sciences · doi:10.1016/j.ins.2017.08.046 · Cited by 44

Fuzzy Recurrent Stochastic Configuration Networks for Industrial Data Analytics

Wang, D., & Dang, G. (2024) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2024.3511695 · Cited by 43

Identification method for a class of periodic discrete-time dynamic nonlinear systems based on Sinusoidal ESN

Yao, X., Wang, Z., & Zhang, H. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.09.092 · Cited by 44

Chaotic time series prediction using echo state network based on selective opposition grey wolf optimizer

Chen, H.-C., & Wei, D. (2021) · Nonlinear Dynamics · doi:10.1007/s11071-021-06452-w · Cited by 43

Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing

Buteneers, P., Verstraeten, D., van Mierlo, P., Wyckhuys, T., Stroobandt, D., Raedt, R., Hallez, H., & Schrauwen, B. (2011) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2011.08.006 · Cited by 43

Reaching movement generation with a recurrent neural network based on learning inverse kinematics for the humanoid robot iCub

Reinhart, F., & Steil, J. J. (2009) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/ichr.2009.5379558 · Cited by 42

Information dynamics based self-adaptive reservoir for delay temporal memory tasks

Dasgupta, S., Wörgötter, F., & Manoonpong, P. (2013) · Evolving Systems · doi:10.1007/s12530-013-9080-y · Cited by 41

An I–V model based on time warp invariant echo state network for photovoltaic array with shaded solar cells

Lun, S., Wang, S., Guo, T., & Du, C.-J. (2014) · Solar Energy · doi:10.1016/j.solener.2014.04.023 · Cited by 42

Destination prediction using deep echo state network

Song, Z., Wu, K., & Shao, J. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.09.115 · Cited by 42

Reservoir Computing with Neuromemristive Nanowire Networks

Fu, K., Zhu, R., Loeffler, A., Hochstetter, J., Álvarez, A. D., Stieg, A. Z., Gimzewski, J. K., Nakayama, T., & Kuncic, Z. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207727 · Cited by 44

Exploiting Multiple Timescales in Hierarchical Echo State Networks

Manneschi, L., Ellis, M. O. A., Gigante, G., Lin, A. C., Giudice, P. D., & Vasilaki, E. (2021) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2020.616658 · Cited by 42

A modified batch intrinsic plasticity method for pre-training the random coefficients of extreme learning machines

Dong, S. S., & Li, Z. (2021) · Journal of Computational Physics · doi:10.1016/j.jcp.2021.110585 · Cited by 48

Modified dynamic surface approach with bias torque for multi-motor servomechanism

Wang, M., Ren, X., Chen, Q., Wang, S., & Gao, X. (2016) · Control Engineering Practice · doi:10.1016/j.conengprac.2016.02.011 · Cited by 41

Brain-inspired computational paradigm dedicated to fault diagnosis of PEM fuel cell stack

Zheng, Z., Morando, S., Péra, M., Hissel, D., Larger, L., Martinenghi, R., & Baylón-Fuentes, A. (2017) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2016.11.043 · Cited by 41

Dual Heuristic Programming for Optimal Control of Continuous-Time Nonlinear Systems Using Single Echo State Network

Liu, C., Zhang, H., Luo, Y., & Su, H. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2984952 · Cited by 40

A learning system for automatic Berg Balance Scale score estimation

Bacciu, D., Chessa, S., Gallicchio, C., Micheli, A., Pedrelli, L., Ferro, E., Fortunati, L., Rosa, D. L., Palumbo, F., Vozzi, F., et al. (2017) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2017.08.018 · Cited by 41

Feedback-Driven Quantum Reservoir Computing for Time-Series Analysis

Kobayashi, K., Fujii, K., & Yamamoto, N. (2024) · PRX Quantum · doi:10.1103/prxquantum.5.040325 · Cited by 58

A Literature Review on Some Trends in Artificial Neural Networks for Modeling and Simulation with Time Series

Zavala, Á. E. M., Macías‐Díaz, J. E., Alba-Cuéllar, D., & Guerrero-Díaz-de-León, J. A. (2024) · Algorithms · doi:10.3390/a17020076 · Cited by 45

Reservoir computing benchmarks: a tutorial review and critique

Wringe, C., Trefzer, M. A., & Stepney, S. (2025) · International Journal of Parallel Emergent and Distributed Systems · doi:10.1080/17445760.2025.2472211 · Cited by 43

Utilizing online learning based on echo-state networks for the control of a hydraulic excavator

Park, J., Cho, D., Kim, S.-H., Kim, Y. B., Kim, P. Y., & Kim, H. J. (2014) · Mechatronics · doi:10.1016/j.mechatronics.2014.10.004 · Cited by 38

Deep reservoir computing: A critical analysis

Gallicchio, C., & Micheli, A. (2016) · CINECA IRIS Institutial research information system (University of Pisa) · Cited by 38

A deep learning based approach for analog hardware implementation of delayed feedback reservoir computing system

Li, J., Bai, K., Liu, L., & Yi, Y. (2018) · International Symposium on Quality Electronic Design (ISQED) · doi:10.1109/isqed.2018.8357305 · Cited by 36

Evolving Local Plasticity Rules for Synergistic Learning in Echo State Networks

Wang, X., Jin, Y., & Hao, K. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2919903 · Cited by 37

Prediction of the Italian electricity price for smart grid applications

Crisostomi, E., Gallicchio, C., Micheli, A., Raugi, M., & Tucci, M. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.02.089 · Cited by 38

Echo State Networks: Novel reservoir selection and hyperparameter optimization model for time series forecasting

Niño, C. H. V., Vellasco, M. M. B. R., & Figueiredo, K. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126317 · Cited by 36

Multi-step-ahead Host Load Prediction with GRU Based Encoder-Decoder in Cloud Computing

Peng, C., Li, Y., Yu, Y., Zhou, Y., & Du, S. (2018) · International Conference on Knowledge and Smart Technology · doi:10.1109/kst.2018.8426104 · Cited by 38

Delay independent robust stability analysis of delayed fractional quaternion-valued leaky integrator echo state neural networks with QUAD condition

Pahnehkolaei, S. M. A., Alfi, A., & Machado, J. A. T. (2019) · Applied Mathematics and Computation · doi:10.1016/j.amc.2019.04.083 · Cited by 37

Fault prognostics by an ensemble of Echo State Networks in presence of event based measurements

Xu, M., Baraldi, P., Al‐Dahidi, S., & Zio, E. (2019) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2019.103346 · Cited by 36

Chain-Structure Echo State Network With Stochastic Optimization: Methodology and Application

Wu, Z., Li, Q., & Zhang, H. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3098866 · Cited by 37

The Statistical Recurrent UnitPreprint

Oliva, J. B., Póczos, B., & Schneider, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.00381 · Cited by 41

Recurrent temporal networks and language acquisition—from corticostriatal neurophysiology to reservoir computing

Dominey, P. F. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00500 · Cited by 35

Echo state networks regulated by local intrinsic plasticity rules for regression

Wang, X., Jin, Y., & Hao, K. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.03.032 · Cited by 36

An echo state network architecture based on quantum logic gate and its optimization

Liu, J., Sun, T., Luo, Y., Yang, S., Cao, Y., & Zhai, J. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.09.002 · Cited by 36

Echo state network activation function based on bistable stochastic resonance

Liao, Z., Wang, Z., Yamahara, H., & Tabata, H. (2021) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2021.111503 · Cited by 36

An Echo State Network With Improved Topology for Time Series Prediction

Li, X., Bi, F., Yang, X. C., & Bi, X. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3148742 · Cited by 37

Robust echo state networks based on correntropy induced loss function

Guo, Y., Wang, F., Chen, B., & Xin, J. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.05.087 · Cited by 35

Optimal short-term memory before the edge of chaos in driven random recurrent networks

Haruna, T., & Nakajima, K. (2019) · Physical review. E · doi:10.1103/physreve.100.062312 · Cited by 35

Insight into delay based reservoir computing via eigenvalue analysis

Köster, F., Yanchuk, S., & Lüdge, K. (2021) · Journal of Physics Photonics · doi:10.1088/2515-7647/abf237 · Cited by 35

Nonlinear Model Predictive Control of Electrical Submersible Pumps based on Echo State Networks

Jordanou, J. P., Osnes, I., Hernes, S. B., Camponogara, E., Antonelo, E. A., & Imsland, L. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101553 · Cited by 35

Echo state network with multiple delayed outputs for multiple delayed time series prediction

Yao, X., Shao, Y., Fan, S., & Cao, S. (2022) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2022.09.059 · Cited by 35

Band-pass Reservoir Computing

wyffels, F., Schrauwen, B., Verstraeten, D., & Stroobandt, D. (2008) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4634252 · Cited by 34

Parameters selection method of the leaky integrator echo state network for time series

Yao, X., & Lun, S. (2014) · International Conference on Mechatronics and Control · doi:10.1109/icmc.2014.7231760 · Cited by 3

The modified sufficient conditions for echo state property and parameter optimization of leaky integrator echo state network

Lun, S., Hu, H., & Yao, X. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.02.005 · Cited by 29

Re-visiting the echo state property

Yildiz, I. B., Jaeger, H., & Kiebel, S. J. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.07.005 · Cited by 495

The combination of circle topology and leaky integrator neurons remarkably improves the performance of echo state network on time series prediction

Xue, F., Li, Q., & Li, X. (2017) · PLoS ONE · doi:10.1371/journal.pone.0181816 · Cited by 16

Parameters relation of the Leaky integrator echo state network for time series

Qi, H., & Lun, S. (2015) · Chinese Automation Congress · doi:10.1109/cac.2015.7382604