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

Nonlinear Model Predictive Control of an Oil Well with Echo State Networks

Jordanou, J. P., Camponogara, E., Antonelo, E. A., & de Aguiar, M. A. S. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.06.348 · Cited by 34

Stability analysis of fractional Quaternion-Valued Leaky Integrator Echo State Neural Networks with multiple time-varying delays

Pahnehkolaei, S. M. A., Alfi, A., & Machado, J. A. T. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.11.021 · Cited by 34

Effect of hybrid circle reservoir injected with wavelet-neurons on performance of echo state network

Cui, H., Feng, C., Chai, Y., Liu, R. P., & Liu, Y. (2014) · Neural Networks · doi:10.1016/j.neunet.2014.05.013 · Cited by 33

Achieving stable dynamics in neural circuits

Kozachkov, L., Lundqvist, M., Slotine, J.-J. E., & Miller, E. K. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007659 · Cited by 34

Deep-ESN: A Multiple Projection-encoding Hierarchical Reservoir Computing FrameworkPreprint

Ma, Q., Shen, L., & Cottrell, G. W. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.05255 · Cited by 44

Spatio-temporal modeling with enhanced flexibility and robustness of solar irradiance prediction: A chain-structure echo state network approach

Li, Q., Wu, Z., & Zhang, H. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.121151 · Cited by 32

Tailoring Echo State Networks for Optimal Learning

Aceituno, P. V., Yan, G. F., & Liu, Y. (2020) · iScience · doi:10.1016/j.isci.2020.101440 · Cited by 38

Numerical Computation of Partial Differential Equations by Hidden-Layer Concatenated Extreme Learning Machine

Ni, N., & Dong, S. S. (2023) · Journal of Scientific Computing · doi:10.1007/s10915-023-02162-0 · Cited by 35

Prediction of Air Pollution Concentration Based on mRMR and Echo State Network

Xu, X., & Ren, W. (2019) · Applied Sciences · doi:10.3390/app9091811 · Cited by 30

Prediction of blood glucose concentration for type 1 diabetes based on echo state networks embedded with incremental learning

Li, N., Tuo, J., Wang, Y., & Wang, M. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.10.003 · Cited by 31

An Improved Grasshopper Optimization Algorithm Based Echo State Network for Predicting Faults in Airplane Engines

Bala, A., Ismail, I. B., Ibrahim, R., Sait, S. M., & Oliva, D. A. (2020) · IEEE Access · doi:10.1109/access.2020.3020356 · Cited by 31

A Cerebellum-Inspired Network Model and Learning Approaches for Solving Kinematic Tracking Control of Redundant Manipulators

Tan, N., Yu, P., & Ni, F. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2022.3149622 · Cited by 31

Regularization and stability in reservoir networks with output feedback

Reinhart, F., & Steil, J. J. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.01.032 · Cited by 31

Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems

Grigoryeva, L., & Ortega, J. (2017) · Alexandria (UniSG) (University of St.Gallen) · doi:10.48550/arxiv.1712.00754 · Cited by 29

Carbon price forecasting using leaky integrator echo state networks with the framework of decomposition-reconstruction-integration

Bai, Y., Deng, S., Pu, Z., & Li, C. (2024) · Energy · doi:10.1016/j.energy.2024.132338 · Cited by 30

Self-Organizing Maps and Scale-Invariant Maps in Echo State Networks

Basterrech, S., Fyfe, C., & Gerardo Rubino, C. (2011) · International Conference on Intelligent Systems Design and Applications (ISDA) · doi:10.1109/isda.2011.6121637 · Cited by 30

A Novel Echo State Network and Its Application in Temperature Prediction of Exhaust Gas From Hot Blast Stove

Yang, Y., Xin, Z., & Liu, X. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3003975 · Cited by 30

Modeling hydrogen solubility in alcohols using machine learning models and equations of state

Mohammadi, M.-R., Hadavimoghaddam, F., Atashrouz, S., Abedi, A., Hemmati‐Sarapardeh, A., & Mohaddespour, A. (2021) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2021.117807 · Cited by 30

Clustering and dynamic recognition based auto-reservoir neural network: A wait-and-see approach for short-term park power load forecasting

Liu, J., Chen, J., Yan, G., Chen, W., & Xu, B. (2023) · iScience · doi:10.1016/j.isci.2023.107456 · Cited by 30

A Novel Neural Networks Ensemble Approach for Modeling Electrochemical Cells

Luzi, M., Paschero, M., Rizzi, A., Maiorino, E., & Mascioli, F. M. F. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2827307 · Cited by 30

Fuzzy Classification With Restricted Boltzman Machines and Echo-State Networks for Predicting Potential Railway Door System Failures

Fink, O., Zio, E., & Weidmann, U. (2015) · IEEE Transactions on Reliability · doi:10.1109/tr.2015.2424213 · Cited by 29

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

Online learning control with Echo State Networks of an oil production platform

Jordanou, J. P., Antonelo, E. A., & Camponogara, E. (2019) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2019.06.011 · Cited by 30

Echo State Queueing Network: A new reservoir computing learning tool

Basterrech, S., & Rubino, G. (2013) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc.2013.6488435 · Cited by 29

A brain-inspired computational model for spatio-temporal information processing

Lin, X., Zou, X., Ji, Z., Huang, T., Wu, S., & Mi, Y. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.05.015 · Cited by 30

Continuum-Body-Pose Estimation From Partial Sensor Information Using Recurrent Neural Networks

Tanaka, K., Minami, Y., Tokudome, Y., Inoue, K., Kuniyoshi, Y., & Nakajima, K. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3199034 · Cited by 28

Multi-Reservoir Echo State Networks with Hodrick–Prescott Filter for nonlinear time-series prediction

Li, Z., Liu, Y., & Tanaka, G. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110021 · Cited by 31

Unifying quality metrics for reservoir networks

Gibbons, T. E. (2010) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2010.5596307 · Cited by 28

Configured quantum reservoir computing for multi-task machine learning

Xia, W., Zou, J., Qiu, X., Chen, F., Zhu, B., Li, C., Deng, D.-L., & Li, X. (2023) · 中国科学通报：英文版 · doi:10.1016/j.scib.2023.08.040 · Cited by 27

Physical reservoir computing: a tutorial

Stepney, S. (2024) · Natural Computing · doi:10.1007/s11047-024-09997-y · Cited by 30

Know Your Body Through Intrinsic Goals

Mannella, F., Santucci, V. G., Somogyi, E., Jacquey, L., O'Regan, J. K., & Baldassarre, G. (2018) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2018.00030 · Cited by 27

SpaRCe: Improved Learning of Reservoir Computing Systems Through Sparse Representations

Manneschi, L., Lin, A. C., & Vasilaki, E. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3102378 · Cited by 30

TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data

Sun, C., Hong, S., Song, M., Chou, Y.-H., Sun, Y., Cai, D., & Li, H. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/414 · Cited by 27

Single- and Multiagent Actor–Critic for Initial UAV’s Deployment and 3-D Trajectory Design

Nasr-Azadani, M., Abouei, J., & Plataniotis, K. N. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3150184 · Cited by 27

Attention based English to Punjabi neural machine translation

Singh, S., Kumar, M. A., & Soman, K. P. (2018) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-169450 · Cited by 29

Learning from the past: reservoir computing using delayed variables

Parlitz, U. (2024) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2024.1221051 · Cited by 26

Echo Memory-Augmented Network for time series classification

Ma, Q., Zheng, Z., Zhuang, W., Chen, E., Wei, J., & Wang, J. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.10.015 · Cited by 26

Robust face tracking control of a mobile robot using self‐tuning Kalman filter and echo state network

Tsai, C., Dutoit, X., Song, K., BRUSSEL, H. V., & Nuttin, M. (2010) · Asian Journal of Control · doi:10.1002/asjc.204 · Cited by 26

Reservoir Computing and Self-Organized Neural Hierarchies

Lukoševičius, M. (2012) · Cited by 26

Recurrent Neural Associative Learning of Forward and Inverse Kinematics for Movement Generation of the Redundant PA-10 Robot

Reinhart, F., & Steil, J. J. (2008) · ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems (LAB-RS) · doi:10.1109/lab-rs.2008.17 · Cited by 26

Bayesian optimization of hyper-parameters in reservoir computingPreprint

Yperman, J., & Becker, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.05193 · Cited by 41

On prediction of chaotic dynamics in semiconductor lasers by reservoir computing

Li, X., Yang, B., Zhao, S., Gu, Y., & Zhao, M. (2023) · Optics Express · doi:10.1364/oe.504792 · Cited by 25

Fractional-integer-order echo state network for time series prediction

Yao, X., Wang, Y., Ma, D., Cao, S., & Ma, Q. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111289 · Cited by 25

Residual Echo State Networks: Residual recurrent neural networks with stable dynamics and fast learning

Ceni, A., & Gallicchio, C. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127966 · Cited by 28

A fault diagnosis method for offshore wind turbine bearing based on adaptive deep echo state network and bidirectional long short term memory network in noisy environment

Du, Y., Geng, X., Zhou, Q., & Cheng, S. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.119101 · Cited by 26

Reservoir computing in materio: An evaluation of configuration through evolution

Dale, M., Stepney, S., Miller, J. F., & Trefzer, M. A. (2016) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2016.7850170 · Cited by 25

Monitor-Based Spiking Recurrent Network for the Representation of Complex Dynamic Patterns

Hu, R., Huang, Q., Wang, H., He, J. L., & Chang, S. (2019) · International Journal of Neural Systems · doi:10.1142/s0129065719500060 · Cited by 26

Game Theory for Next Generation Wireless and Communication Networks: Modeling, Analysis, and Design

Han, Z., Niyato, D. T., Saad, W., & Başar, T. (2019) · DR-NTU (Nanyang Technological University) · Cited by 25

The echo index and multistability in input-driven recurrent neural networks

Ceni, A., Ashwin, P., Livi, L., & Postlethwaite, C. (2020) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2020.132609 · Cited by 29

Efficient Implementations of Echo State Network Cross-Validation

Lukoševičius, M., & Uselis, A. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09849-2 · Cited by 25

Automatic topology optimization of echo state network based on particle swarm optimization

Xue, Y., Zhang, Q., & Słowik, A. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105574 · Cited by 25

Adaptive Fuzzy Echo State Network Control of Fractional-Order Large-Scale Nonlinear Systems With Time-Varying Deferred Constraints

Wang, Q., Pan, Y., Cao, J., & Liu, H. (2023) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2023.3305606 · Cited by 25

Multiscale Model of an Inhibitory Network Shows Optimal Properties near Bifurcation

Buckley, C. L., & Nowotny, T. (2011) · Physical Review Letters · doi:10.1103/physrevlett.106.238109 · Cited by 25

Dimension of reservoir computers

Carroll, T. L. (2020) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.5128898 · Cited by 25

Prediction of chaotic dynamics and extreme events: A recurrence-free quantum reservoir computing approach

Ahmed, O., Tennie, F., & Magri, L. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.043082 · Cited by 24

Production Analysis and Forecasting for Unconventional Reservoirs Using Laplacian Echo-State Networks

Pan, Y., Zhou, P., Deng, L., & Lee, J. (2019) · SPE Western Regional Meeting · doi:10.2118/195243-ms · Cited by 24

Chaotic time series prediction using phase space reconstruction based conceptor network

Zhang, A., & Zheng, X. (2020) · Cognitive Neurodynamics · doi:10.1007/s11571-020-09612-7 · Cited by 24

Predicting chaotic time series and replicating chaotic attractors based on two novel echo state network models

Li, Y., & Li, Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.03.054 · Cited by 24

Long-term ENSO prediction with echo-state networks

Hassanibesheli, F., Kurths, J., & Boers, N. (2022) · Environmental Research Climate · doi:10.1088/2752-5295/ac7f4c · Cited by 28

DeepFESL: Deep Federated Echo State Learning-Based Proactive Content Caching in UAV-Assisted Networks

Maale, G. T., Sun, G., Kuadey, N. A., Kwantwi, T., Ou, R., & Liu, G. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3268541 · Cited by 24

Recent advances in efficient learning of recurrent networks.

Hammer, B., Schrauwen, B., & Steil, J. J. (2009) · Cited by 24

Real-Time Epileptic Seizure Detection on Intra-cranial Rat Data Using Reservoir Computing

Buteneers, P., Schrauwen, B., Verstraeten, D., & Stroobandt, D. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02490-0_7 · Cited by 24

Learning context-aware mobile robot navigation in home environments

Bacciu, D., Gallicchio, C., Micheli, A., Rocco, M. D., & Saffiotti, A. (2014) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa.2014.6878733 · Cited by 24

Utilization of Echo State Networks for Differentiating Source and Nonlinear Load Harmonics in the Utility Network

Mazumdar, J., & Harley, R. G. (2008) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2008.2005097 · Cited by 24

A computational Intelligence-based Method to ‘Learn’ Causal Loop Diagram-like Structures from Observed Data

Abdelbari, H., & Shafi, K. (2017) · System Dynamics Review · doi:10.1002/sdr.1567 · Cited by 24

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Bitzer, S., & Kiebel, S. J. (2012) · Biological Cybernetics · doi:10.1007/s00422-012-0490-x · Cited by 24

Optimally fuzzy temporal memoryPreprint

Shankar, K. H., & Howard, M. W. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1211.5189 · Cited by 24

Anti-oscillation and chaos control of the fractional-order brushless DC motor system via adaptive echo state networks

Luo, S., Li, S., Tajaddodianfar, F., & Hu, J. (2018) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2018.07.004 · Cited by 23

Hourly Urban Water Demand Forecasting Using the Continuous Deep Belief Echo State Network

Xu, Y., Zhang, J., Long, Z., Tang, H., & Zhang, X. (2019) · Water · doi:10.3390/w11020351 · Cited by 23

Leakage-Aware Predictive Thermal Management for Multicore Systems Using Echo State Network

Wang, H., Guo, X., Tan, S. X.-D., Zhang, C., Tang, H., & Yuan, Y. (2019) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2019.2915316 · Cited by 23

Efficient Optimization of Echo State Networks for Time Series Datasets

Maat, J. R., Gianniotis, N., & Protopapas, P. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489094 · Cited by 23

RNNs Incrementally Evolving on an Equilibrium Manifold: A Panacea for Vanishing and Exploding Gradients?

Kag, A., Zhang, Z., & Saligrama, V. (2020) · International Conference on Learning Representations · Cited by 23

Ensemble echo network with deep architecture for time-series modeling

Hu, R., Tang, Z., Song, X., Luo, J., Wu, E. Q., & Chang, S. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05286-8 · Cited by 23

Fractional-Order Echo State Network Backstepping Control of Fractional-Order Nonlinear Systems

Liu, H., Shi, J., Cao, J., & Pan, Y. (2023) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2023.3309739 · Cited by 23

An Online Learning Strategy for Echo State Network

Chen, X., Jin, L., & Li, S. (2023) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2023.3319357 · Cited by 23

User Movements Forecasting by Reservoir Computing Using Signal Streams Produced by Mote-Class Sensors

Gallicchio, C., Micheli, A., Barsocchi, P., & Chessa, S. (2012) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-642-29479-2_12 · Cited by 23

Multilayered Echo-State Machine: A Novel Architecture for Efficient Intrusion Detection

Tchakoucht, T. A., & Ezziyyani, M. (2018) · IEEE Access · doi:10.1109/access.2018.2867345 · Cited by 23

Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State NetworksPreprint

Anantharaman, R., Ma, Y., Gowda, S., Laughman, C., Shah, V. B., Edelman, A. S., & Rackauckas, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.04004 · Cited by 23

Random Orthogonal Additive Filters: A Solution to the Vanishing/Exploding Gradient of Deep Neural Networks

Ceni, A. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3538924 · Cited by 28

Robust continuous digit recognition using Reservoir Computing

Jalalvand, A., Triefenbach, F., Demuynck, K., & Martens, J. (2014) · Computer Speech & Language · doi:10.1016/j.csl.2014.09.006 · Cited by 21

Structure optimization of reservoir networks

Roeschies, B., & Igel, C. (2009) · Logic Journal of IGPL · doi:10.1093/jigpal/jzp043 · Cited by 25

Fractional Order Echo State Network for Time Series Prediction

Yao, X., & Wang, Z. (2020) · Neural Processing Letters · doi:10.1007/s11063-020-10267-y · Cited by 22

Online learning on tiny micro-controllers for anomaly detection in water distribution systems

Pau, D., Khiari, A., & Denaro, D. (2021) · 2021 IEEE 11th International Conference on Consumer Electronics (ICCE-Berlin) · doi:10.1109/icce-berlin53567.2021.9720009 · Cited by 21

Computational Modeling of Structural Synaptic Plasticity in Echo State Networks

Wang, X., Jin, Y., & Hao, K. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3060466 · Cited by 22

Evolutionary optimization assisted delayed deep cycle reservoir modeling method with its application to ship heave motion prediction

Huang, L., Deng, X., Bo, Y.-C., Zhang, Y., & Wang, P. (2021) · ISA Transactions · doi:10.1016/j.isatra.2021.08.020 · Cited by 21

Direct adaptive control for nonlinear systems using a TSK fuzzy echo state network based on fractional-order learning algorithm

Mahmoud, T. A., Abdo, M. I., Elsheikh, E. A., & Elshenawy, L. M. (2021) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2021.09.015 · Cited by 21

An Improved Fuzzy Recurrent Stochastic Configuration Network for Modeling Nonlinear Systems

Dang, G., & Wang, D. (2024) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2024.3513394 · Cited by 22

Robot Localization by Echo State Networks Using RSS

Chessa, S., Gallicchio, C., Guzmán, R., & Micheli, A. (2013) · Smart innovation, systems and technologies · doi:10.1007/978-3-319-04129-2_15 · Cited by 21

Nonlinear Memory Capacity of Parallel Time-Delay Reservoir Computers in the Processing of Multidimensional Signals

Grigoryeva, L., Henriques, J., Larger, L., & Ortega, J. (2016) · Neural Computation · doi:10.1162/neco_a_00845 · Cited by 21

Enhancing Group Social Perceptiveness through a Swarm-based Decision-Making Platform

Askay, D. A., Metcalf, L. E., Rosenberg, L. B., & Willcox, G. (2019) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2019.061 · Cited by 21

Predicting time series of railway speed restrictions with time-dependent machine learning techniques

Fink, O., Zio, E., & Weidmann, U. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.04.038 · Cited by 21

A machine-learning approach for long-term prediction of experimental cardiac action potential time series using an autoencoder and echo state networks

Shahi, S., Fenton, F. H., & Cherry, E. M. (2022) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0087812 · Cited by 20

On-Device Training of Deep Learning Models on Edge Microcontrollers

Vita, F. D., Nocera, G., Bruneo, D., Tomaselli, V., & Falchetto, M. (2022) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics55523.2022.00018 · Cited by 20

Online Self-Learning Fuzzy Recurrent Stochastic Configuration Networks for Modeling Nonstationary Dynamics

Dang, G., & Wang, D. (2025) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2025.3532652 · Cited by 20

Effects of data cleansing on load prediction algorithms

Høverstad, B. A., Tidemann, A., & Helge Langseth, A. (2013) · IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG) · doi:10.1109/ciasg.2013.6611504 · Cited by 20

Predicting User Movements in Heterogeneous Indoor Environments by Reservoir Computing

Bacciu, D., Chessa, S., Gallicchio, C., Barsocchi, P., & Micheli, A. (2011) · CINECA IRIS Institutial research information system (University of Pisa) · Cited by 20

Echo State Speech Recognition

Shrivastava, H., Garg, A., Cao, Y., Zhang, Y., & Sainath, T. N. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414495 · Cited by 19

Laplacian Echo-State Networks for production analysis and forecasting in unconventional reservoirs

Pan, Y., Deng, L., Zhou, P., & Lee, W. J. (2021) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2021.109068 · Cited by 20

Echo State Network ship motion modeling prediction based on Kalman filter

Peng, X., Dong, H., & Zhang, B. (2017) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma.2017.8015795 · Cited by 20

Identification and Prediction of Urban Traffic Congestion via Cyber-Physical Link Optimization

Huang, D., Deng, Z., Wan, S., Mi, B., & Liu, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2875239 · Cited by 20

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

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