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

Deep Echo State Networks for Diagnosis of Parkinson's DiseasePreprint

Gallicchio, C., Micheli, A., & Pedrelli, L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.06708 · Cited by 20

RESERVOIR COMPUTING: A POWERFUL BLACK-BOX FRAMEWORK FOR NONLINEAR AUDIO PROCESSING

Holzmann, G. (2009) · Cited by 19

Regularized Taylor Echo State Networks for Predictive Control of Partially Observed Systems

Xiang, K., Li, B. N., Zhang, L., Pang, M., Wang, M., & Li, X. (2016) · IEEE Access · doi:10.1109/access.2016.2582478 · Cited by 19

Hierarchical Temporal Representation in Linear Reservoir Computing

Gallicchio, C., Micheli, A., & Pedrelli, L. (2018) · Smart innovation, systems and technologies · doi:10.1007/978-3-319-95098-3_11 · Cited by 19

Optimizing Echo State Networks for Enhancing Large Prediction Horizons of Chaotic Time Series

González-Zapata, A. M., Tlelo‐Cuautle, E., Ovilla-Martínez, B., Cruz-Vega, I., & de la Fraga, L. G. (2022) · Mathematics · doi:10.3390/math10203886 · Cited by 19

Multiscale echo self-attention memory network for multivariate time series classification

Lyu, H., Huang, D., Li, S., Ng, W. W. Y., & Ma, Q. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.11.066 · Cited by 19

Terrain Classification for a Quadruped Robot

Degrave, J., Cauwenbergh, R. V., wyffels, F., Waegeman, T., & Schrauwen, B. (2013) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2013.39 · Cited by 19

Algebraic approach towards the exploitation of “softness”: the input–output equation for morphological computation

Komatsu, M., Yaguchi, T., & Nakajima, K. (2020) · The International Journal of Robotics Research · doi:10.1177/0278364920912298 · Cited by 19

On the post-hoc explainability of deep echo state networks for time series forecasting, image and video classification

Arrieta, A. B., Gil-López, S., Laña, I., Bilbao, M. N., & Ser, J. D. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06359-y · Cited by 21

Time Adaptive Recurrent Neural Network

Kag, A., & Saligrama, V. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01490 · Cited by 19

Cluster-Based Input Weight Initialization for Echo State Networks

Steiner, P., Jalalvand, A., & Birkholz, P. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3145565 · Cited by 19

Event Detection and Localization in Mobile Robot Navigation Using Reservoir Computing

Antonelo, E. A., Schrauwen, B., Dutoit, X., Stroobandt, D., & Nuttin, M. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74695-9_68 · Cited by 19

Optimizing echo state network through a novel fisher maximization based stochastic gradient descent

Öztürk, M. M., Çankaya, İ. A., & İpekçi, D. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.07.034 · Cited by 18

Synergies between synaptic and intrinsic plasticity in echo state networks

Wang, X., Jin, Y., & Hao, K. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.12.007 · Cited by 18

Sentiment analysis and causal learning of COVID-19 tweets prior to the rollout of vaccines

Zhang, Q., Yi, G. Y., Chen, L., & He, W. (2023) · PLoS ONE · doi:10.1371/journal.pone.0277878 · Cited by 18

TSK fuzzy echo state neural network: a hybrid structure for black-box nonlinear systems identification

Mahmoud, T. A., & Elshenawy, L. M. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06838-2 · Cited by 18

Edge of Stability Echo State Network

Ceni, A., & Gallicchio, C. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3400045 · Cited by 18

Broad fractional-order echo state network with slime mould algorithm for multivariate time series prediction

Yao, X., Wang, H., & Huang, Z. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111900 · Cited by 18

Recurrent sparse support vector regression machines trained by active learning in the time-domain

Čeperić, V., Gielen, G. E., & Barić, A. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.03.031 · Cited by 18

Nonlinear process modelling using echo state networks optimised by covariance matrix adaption evolutionary strategy

Liu, K., & Zhang, J. (2020) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2020.106730 · Cited by 18

Application of ESN prediction model based on compressed sensing in stock market

Zhang, H., Zheng, M., Zhang, Y., Yu, X., Li, W., & Gao, H. (2021) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2021.105857 · Cited by 18

Cortico-striatal language pathways dynamically adjust for syntactic complexity: A computational study

Szalisznyó, K., Silverstein, D. N., Teichmann, M., Duffau, H., & Smits, A. (2016) · Brain and Language · doi:10.1016/j.bandl.2016.08.005 · Cited by 18

Predicting the fMRI Signal Fluctuation with Recurrent Neural Networks Trained on Vascular Network Dynamics

Sobczak, F., He, Y., B, T. J. S. A., & Yu, X. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa260 · Cited by 18

Direct Control of an Active Tactile Sensor Using Echo State Networks

Krause, A. F., Bläsing, B. E., Dürr, V., & Schack, T. (2009) · Cognitive systems monographs · doi:10.1007/978-3-642-10403-9_2 · Cited by 17

Adjustable short-term memory of SiO x :Ag-based memristor for reservoir computing

Li, R., Yang, H., Zhang, Y., Tang, N., Chen, R., Zhou, Z., Liu, L., Kang, J., & Huang, P. C. (2023) · Nanotechnology · doi:10.1088/1361-6528/acfb0a · Cited by 17

Reservoir computing with higher-order interactive coupled pendulums

Li, X., SMALL, M. P., & Lei, Y. (2023) · Physical review. E · doi:10.1103/physreve.108.064304 · Cited by 17

Euler State Networks: Non-dissipative Reservoir Computing

Gallicchio, C. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127411 · Cited by 22

Barrier function-based adaptive fixed-time ADLESN prescribed performance tracking control for UUVs with input saturation

Qi, Z., Liu, H., Tian, X., & Yuan, J. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.117676 · Cited by 17

Impact of time-history terms on reservoir dynamics and prediction accuracy in echo state networks

Ebato, Y., Nobukawa, S., Sakemi, Y., Nishimura, H., Kanamaru, T., Sviridova, N., & Aihara, K. (2024) · Scientific Reports · doi:10.1038/s41598-024-59143-y · Cited by 17

A systematic study of Echo State Networks topologies for chaotic time series prediction

Viehweg, J., Teutsch, P., & Mäder, P. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129032 · Cited by 17

Optimal training of finitely sampled quantum reservoir computers for forecasting of chaotic dynamics

Ahmed, O., Tennie, F., & Magri, L. (2025) · Quantum Machine Intelligence · doi:10.1007/s42484-025-00261-9 · Cited by 17

Learning slow features with reservoir computing for biologically-inspired robot localization

Antonelo, E. A., & Schrauwen, B. (2011) · Neural Networks · doi:10.1016/j.neunet.2011.08.004 · Cited by 20

A Constrained Regularization Approach for Input-Driven Recurrent Neural Networks

Reinhart, F., & Steil, J. J. (2010) · Differential Equations and Dynamical Systems · doi:10.1007/s12591-010-0067-x · Cited by 17

WOA-Based Echo State Network for Chaotic Time Series Prediction

Zhang, M., Wang, B., Zhou, Y., & Sun, H. (2020) · Journal of the Korean Physical Society · doi:10.3938/jkps.76.384 · Cited by 17

Design of PID Controller Based on Echo State Network With Time-Varying Reservoir Parameter

Wang, Z., Yao, X., Li, T., & Zhang, H. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3090812 · Cited by 17

Unsupervised Formation of Vocalization-Sensitive Neurons: A Cortical Model Based on Short-Term and Homeostatic Plasticity

Lee, T. P., & Buonomano, D. V. (2012) · Neural Computation · doi:10.1162/neco_a_00345 · Cited by 17

Multi-steps prediction of chaotic time series based on echo state network

Song, Y. H., Li, Y., Wang, Q., & Li, C. (2010) · International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) · doi:10.1109/bicta.2010.5645205 · Cited by 17

Adaptive reservoir computing through evolution and learning

Chatzidimitriou, K. C., & Mitkas, P. A. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.09.022 · Cited by 17

The copula echo state network

Chatzis, S., & Demiris, Y. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.06.022 · Cited by 17

FPGA Implementation of Reservoir Computing with Online Learning

Antonik, P., Smerieri, A., Duport, F., Haelterman, M., & Massar, S. (2015) · Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) · Cited by 17

Optimized echo state networks with leaky integrator neurons for EEG-based microsleep detection

Ayyagari, S. S. D. P., Jones, R. D., & Weddell, S. J. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7319215 · Cited by 17

Why Layering in Recurrent Neural Networks? A DeepESN Survey

Gallicchio, C., & Micheli, A. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489368 · Cited by 17

DeepESN neural networks for industrial predictive maintenance through anomaly detection from production energy Data

Bonci, A., Fredianelli, L., Kermenov, R., Longarini, L., Longhi, S., Pompei, G., Prist, M., & Verdini, C. (2024) · Applied Sciences · doi:10.3390/app14198686 · Cited by 16

A Deep Echo State Network With Scaling Factor Activation Functions for Fault Diagnosis of Electrical Drive Systems

Gong, Y., Jiang, Y., Cheng, C., Chen, H., & Wang, S. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3532950 · Cited by 17

Time series classification models based on nonlinear spiking neural P systems

Xiong, X., Wu, M., He, J., Peng, H. J., Wang, J., Long, X., & Yang, Q. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107603 · Cited by 17

Novel efficient reservoir computing methodologies for regular and irregular time series classification

Li, Z.-L., Andreev, A., Hramov, A., Blyuss, O., & Zaikin, A. (2024) · Nonlinear dynamics · doi:10.1007/s11071-024-10244-3 · Cited by 12

Scalable Deep Learning for Stress and Affect Detection on Resource-Constrained Devices

Ragav, A., Krishna, N. H., Narayanan, N., Thelly, K. J., & Vijayaraghavan, V. (2019) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2019.00261 · Cited by 16

Uncertainty Quantification through Dropout in Time Series Prediction by Echo State Networks

Atencia, M., Stoean, R., & Joya, G. (2020) · Mathematics · doi:10.3390/math8081374 · Cited by 17

Echo State Neural Network Based State Feedback Control for SISO Afine Nonlinear Systems

Mahmoud, T. A., & Elshenawy, L. M. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.09.211 · Cited by 16

Empirical analysis of the necessary and sufficient conditions of the echo state property

Basterrech, S. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7965946 · Cited by 16

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

A nature-inspired biomarker for mental concentration using a single-channel EEG

Basterrech, S., & Krömer, P. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04574-2 · Cited by 16

A Recurrent Neural Network for Multiple Language Acquisition: Starting with English and FrenchPreprint

Hinaut, X., Twiefel, J., Petit, M., Dominey, P. F., & Wermter, S. (2015) · Cited by 17

Online learning compensation control of an electro-hydraulic shaking table using Echo State Networks

Liang, J., Ding, Z., Han, Q., Wu, H., & Ji, J. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106274 · Cited by 15

Predicting multi-parametric dynamics of an externally forced oscillator using reservoir computing and minimal data

Yadav, M., Chauhan, S., Shrimali, M. D., & Stender, M. (2024) · Nonlinear Dynamics · doi:10.1007/s11071-024-10720-w · Cited by 15

Reservoir regularization stabilizes learning of Echo State Networks with output feedback

Reinhart, F., & Steil, J. J. (2011) · Cited by 15

Prediction Using Cuckoo Search Optimized Echo State Network

Bala, A., Ismail, I. B., Ibrahim, R., Sait, S. M., & Salami, H. O. (2019) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-019-04008-0 · Cited by 15

EchoBay

Cerina, L., Santambrogio, M. D., Franco, G., Gallicchio, C., & Micheli, A. (2020) · ACM Transactions on Architecture and Code Optimization · doi:10.1145/3404993 · Cited by 15

Study and Application of Fault Prediction Methods with Improved Reservoir Neural Networks

Zhu, Q., Jia, Y., Peng, D., & Xu, Y. (2014) · Chinese Journal of Chemical Engineering · doi:10.1016/j.cjche.2014.05.016 · Cited by 15

Spatiotemporal signal classification via principal components of reservoir states

Prater, A. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.04.008 · Cited by 15

HP-ESN: Echo State Networks Combined with Hodrick-Prescott Filter for Nonlinear Time-Series Prediction

Li, Z., & Tanaka, G. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206771 · Cited by 15

EEG feature learning with Intrinsic Plasticity based Deep Echo State Network

Fourati, R., Ammar, B., Jin, Y., & Alimi, A. M. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207464 · Cited by 15

Functional identification of biological neural networks using reservoir adaptation for point processes

Gürel, T., Rotter, S., & Egert, U. (2009) · Journal of Computational Neuroscience · doi:10.1007/s10827-009-0176-0 · Cited by 15

An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer

Li, X., Bi, F., Zhang, L., Yang, X. C., & Zhang, G. (2022) · Energies · doi:10.3390/en15031205 · Cited by 14

Short-Term Photovoltaic Power Prediction Using Nonlinear Spiking Neural P Systems

Gao, Y., Wang, J., Guo, L., & Peng, H. J. (2024) · Sustainability · doi:10.3390/su16041709 · Cited by 14

Chaotic recurrent neural networks for brain modelling: A review

Mattera, A., Alfieri, V., Granato, G., & Baldassarre, G. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.107079 · Cited by 19

On robust randomized neural networks for regression: a comprehensive review and evaluation

Freire, A. L., Neto, A. R. R., & Barreto, G. A. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-04994-5 · Cited by 14

Hierarchical-Task Reservoir for Online Semantic Analysis From Continuous Speech

Pedrelli, L., & Hinaut, X. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3095140 · Cited by 14

A stability criterion for discrete-time fractional-order echo state network and its application

Yao, X., Wang, Z., & Huang, Z. (2021) · Soft Computing · doi:10.1007/s00500-020-05489-0 · Cited by 14

Nonlinear MIMO System Identification with Echo-State Networks

Schwedersky, B. B., Flesch, R. C. C., & Dangui, H. A. S. (2022) · Journal of Control Automation and Electrical Systems · doi:10.1007/s40313-021-00874-y · Cited by 14

Evolving Dual-Threshold Bienenstock-Cooper-Munro Learning Rules in Echo State Networks

Wang, X., Jin, Y., Du, W., & Wang, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3184004 · Cited by 15

Rotating machinery faults detection method based on deep echo state network

Li, X., Bi, F., Zhang, L., Lin, J., Bi, X., & Yang, X. C. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109335 · Cited by 15

On the Optimization of Machine Learning Techniques for Chaotic Time Series Prediction

González-Zapata, A. M., Tlelo‐Cuautle, E., & Cruz-Vega, I. (2022) · Electronics · doi:10.3390/electronics11213612 · Cited by 15

Investigation of proper orthogonal decomposition for echo state networks

Jordanou, J. P., Antonelo, E. A., Camponogara, E., & Gildin, E. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126395 · Cited by 13

Evolutionary Echo State Network: A neuroevolutionary framework for time series prediction

Basterrech, S., & Gerardo Rubino, C. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110463 · Cited by 14

Modular state space of echo state network

Ma, Q., & Chen, W. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.06.012 · Cited by 14

Monostable Controllers for Adaptive Behaviour

Buckley, C. L., Fine, P., Bullock, S., & Paolo, E. A. D. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69134-1_11 · Cited by 14

Predictive Coding for Dynamic Visual Processing: Development of Functional Hierarchy in a Multiple Spatiotemporal Scales RNN Model

Choi, M., & Tani, J. (2017) · Neural Computation · doi:10.1162/neco_a_01026 · Cited by 14

Efficient Cross-Validation of Echo State Networks

Lukoševičius, M., & Uselis, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30493-5_12 · Cited by 14

Collective dynamics of rate neurons for supervised learning in a reservoir computing system

Maslennikov, O. V., & Nekorkin, V. I. (2019) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.5119895 · Cited by 15

An Experimental Evaluation of Reservoir Computation for Ambient Assisted Living

Bacciu, D., Chessa, S., Gallicchio, C., Micheli, A., & Barsocchi, P. (2012) · Smart innovation, systems and technologies · doi:10.1007/978-3-642-35467-0_5 · Cited by 14

Temporal Convolution Machines for Sequence Learning

Lockett, A. J. (2009) · Cited by 13

Controller Design Based on Echo State Network with Delay Output for Nonlinear System

Yao, X., Fan, S., Zhao, B., & Cao, S. (2020) · Complexity · doi:10.1155/2020/8643029 · Cited by 13

Robust tracking and vibration suppression for nonlinear two-inertia system via modified dynamic surface control with error constraint

Wang, S., Ren, X., Na, J., & Gao, X. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.03.040 · Cited by 13

Effects of singular value spectrum on the performance of echo state network

Li, F., Wang, X., & Li, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.05.068 · Cited by 13

Chaotic Time Series Prediction Using a Novel Echo State Network Model with Input Reconstruction, Bayesian Ridge Regression and Independent Component Analysis

Nguyen, H. M., Kalra, G., Jun, T., & Kim, D. (2019) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001420510088 · Cited by 13

Phase Space Reconstruction-Based Conceptor Network for Time Series Prediction

Zheng, X., Zhong, L., & Zhang, A. (2019) · IEEE Access · doi:10.1109/access.2019.2952365 · Cited by 13

Spatiotemporal dynamics of a buoyancy-driven turbulent fire

Tokami, T., Hachijo, T., Miyano, T., & Gotoda, H. (2020) · Physical review. E · doi:10.1103/physreve.101.042214 · Cited by 13

Network Traffic Anomaly Detection Based on ML-ESN for Power Metering System

Zhang, S. T., Lin, X.-M., Wu, L., Song, Y. Q., Liao, N., & Liang, Z. (2020) · Mathematical Problems in Engineering · doi:10.1155/2020/7219659 · Cited by 13

Spatiotemporal motor learning with reward-modulated Hebbian plasticity in modular reservoir computing

Kawai, Y., & Asada, M. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126740 · Cited by 13

Exploring deep echo state networks for image classification: a multi-reservoir approach

López-Ortiz, E. J., Perea-Trigo, M., Morillo, L. M. S., Sancho-Caparrini, F., & Vegas-Olmos, J. J. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09656-4 · Cited by 12

Enhancing reservoir predictions of chaotic time series by incorporating delayed values of input and reservoir variables

Fleddermann, L., Herzog, S., & Parlitz, U. (2025) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0258250 · Cited by 12

Forecast-Driven Enhancement of Received Signal Strength (RSS)-Based Localization Systems

Barsocchi, P., Chessa, S., Micheli, A., & Gallicchio, C. (2013) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi2040978 · Cited by 12

Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks

Dixon, M. F. (2020) · Technometrics · doi:10.1080/00401706.2021.1921035 · Cited by 16

EchoVPR: Echo State Networks for Visual Place Recognition

Özdemir, A., Scerri, M., Barron, A. B., Philippides, A. O., Mangan, M., Vasilaki, E., & Manneschi, L. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3150505 · Cited by 12

Broad Echo State Network with Reservoir Pruning for Nonstationary Time Series Prediction

Liu, W., Bai, Y., Jin, X., Wang, X., Su, T., & Kong, J. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/3672905 · Cited by 12

Can Non-Linear Readout Nodes Enhance the Performance of Reservoir-Based Speech Recognizers?

Triefenbach, F., & Martens, J. (2011) · International Conference on Informatics and Computational Intelligence (ICI) · doi:10.1109/ici.2011.50 · Cited by 12

Which Hype for My New Task? Hints and Random Search for Echo State Networks Hyperparameters

Hinaut, X., & Trouvain, N. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86383-8_7 · Cited by 12

Deep Echo State Networks with Multi-Span Features for Nonlinear Time Series Prediction

Li, Z., & Tanaka, G. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207401 · Cited by 12

A time-delayed physical reservoir with various time constants

Yamazaki, Y., & Kinoshita, K. (2024) · Applied Physics Express · doi:10.35848/1882-0786/ad2782 · Cited by 11

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