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Journal articles on the topic 'Echo state networks (ESNs)'

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Published: 3 June 2025

Last updated: 19 July 2025

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Dan, Jingpei, Wenbo Guo, Weiren Shi, Bin Fang, and Tingping Zhang. "Deterministic Echo State Networks Based Stock Price Forecasting." Abstract and Applied Analysis 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/137148.

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Echo state networks (ESNs), as efficient and powerful computational models for approximating nonlinear dynamical systems, have been successfully applied in financial time series forecasting. Reservoir constructions in standard ESNs rely on trials and errors in real applications due to a series of randomized model building stages. A novel form of ESN with deterministically constructed reservoir is competitive with standard ESN by minimal complexity and possibility of optimizations for ESN specifications. In this paper, forecasting performances of deterministic ESNs are investigated in stock price prediction applications. The experiment results on two benchmark datasets (Shanghai Composite Index and S&P500) demonstrate that deterministic ESNs outperform standard ESN in both accuracy and efficiency, which indicate the prospect of deterministic ESNs for financial prediction.

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Shutin, Dmitriy, Christoph Zechner, Sanjeev R. Kulkarni, and H. Vincent Poor. "Regularized Variational Bayesian Learning of Echo State Networks with Delay&Sum Readout." Neural Computation 24, no. 4 (2012): 967–95. http://dx.doi.org/10.1162/neco_a_00253.

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In this work, a variational Bayesian framework for efficient training of echo state networks (ESNs) with automatic regularization and delay&sum (D&S) readout adaptation is proposed. The algorithm uses a classical batch learning of ESNs. By treating the network echo states as fixed basis functions parameterized with delay parameters, we propose a variational Bayesian ESN training scheme. The variational approach allows for a seamless combination of sparse Bayesian learning ideas and a variational Bayesian space-alternating generalized expectation-maximization (VB-SAGE) algorithm for estimating parameters of superimposed signals. While the former method realizes automatic regularization of ESNs, which also determines which echo states and input signals are relevant for “explaining” the desired signal, the latter method provides a basis for joint estimation of D&S readout parameters. The proposed training algorithm can naturally be extended to ESNs with fixed filter neurons. It also generalizes the recently proposed expectation-maximization-based D&S readout adaptation method. The proposed algorithm was tested on synthetic data prediction tasks as well as on dynamic handwritten character recognition.

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Ozturk, Mustafa C., Dongming Xu, and José C. Príncipe. "Analysis and Design of Echo State Networks." Neural Computation 19, no. 1 (2007): 111–38. http://dx.doi.org/10.1162/neco.2007.19.1.111.

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The design of echo state network (ESN) parameters relies on the selection of the maximum eigenvalue of the linearized system around zero (spectral radius). However, this procedure does not quantify in a systematic manner the performance of the ESN in terms of approximation error. This article presents a functional space approximation framework to better understand the operation of ESNs and proposes an information-theoretic metric, the average entropy of echo states, to assess the richness of the ESN dynamics. Furthermore, it provides an interpretation of the ESN dynamics rooted in system theory as families of coupled linearized systems whose poles move according to the input signal dynamics. With this interpretation, a design methodology for functional approximation is put forward where ESNs are designed with uniform pole distributions covering the frequency spectrum to abide by the richness metric, irrespective of the spectral radius. A single bias parameter at the ESN input, adapted with the modeling error, configures the ESN spectral radius to the input-output joint space. Function approximation examples compare the proposed design methodology versus the conventional design.

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Hermans, Michiel, and Benjamin Schrauwen. "Recurrent Kernel Machines: Computing with Infinite Echo State Networks." Neural Computation 24, no. 1 (2012): 104–33. http://dx.doi.org/10.1162/neco_a_00200.

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Echo state networks (ESNs) are large, random recurrent neural networks with a single trained linear readout layer. Despite the untrained nature of the recurrent weights, they are capable of performing universal computations on temporal input data, which makes them interesting for both theoretical research and practical applications. The key to their success lies in the fact that the network computes a broad set of nonlinear, spatiotemporal mappings of the input data, on which linear regression or classification can easily be performed. One could consider the reservoir as a spatiotemporal kernel, in which the mapping to a high-dimensional space is computed explicitly. In this letter, we build on this idea and extend the concept of ESNs to infinite-sized recurrent neural networks, which can be considered recursive kernels that subsequently can be used to create recursive support vector machines. We present the theoretical framework, provide several practical examples of recursive kernels, and apply them to typical temporal tasks.

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Laisa, Cristina Juffo Campos, Betencurte da Silva Wellington, Carolina Spindola Rangel Dias Ana, and Cesar Sampaio Dutra Julio. "Exploring Digital Twins of Nonlinear Systems through Meta-Modeling with Echo State Networks." Latin-American Journal of Computing 11, no. 2 (2024): 13–22. https://doi.org/10.5281/zenodo.12169048.

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Effective process monitoring, and control rely on precise dynamic models<strong> </strong>can capture the inherent nonlinearities of chemical systems. However, rigorous modeling of complex industrial processes can be computationally demanding. Meta modeling using machine learning methodologies offers a viable approach to generate computationally efficient surrogate representations. Specifically, Echo State Networks (ESNs) are a promising neural network approach for meta-modeling nonlinear dynamical systems. ESNs simplify training through fixed input weights while they focus learning on output weights. This study explores the development of ESN-based digital twins for a nonlinear dynamic process. An ESN is employed to construct a meta-model of a simulated continuously stirred tank reactor with biochemical kinetic. The network was trained on input-output data obtained from the simulation of an ordinary differential equation system, and the performance was evaluated both in-sample and out-of-sample. The results indicate that the ESN meta-model can successfully approximate the underlying dynamics, accurately capturing temporal evolution. A closed-loop digital twin deployment using the ESN surrogate also showed reliable behavior. This work presents initial steps toward developing digital twins of chemical processes using ESN-driven meta-modeling. The findings suggest ESNs can effectively generate computationally efficient surrogate representations of nonlinear dynamical systems. Such digital twins hold promise for online process monitoring and optimized control of industrial plants.

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Di, Sarli, Claudio Gallicchio, and Alessio Micheli. "On the effectiveness of Gated Echo State Networks for data exhibiting long-term dependencies." Computer Science and Information Systems 19, no. 1 (2022): 379–96. http://dx.doi.org/10.2298/csis210218063d.

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In the context of recurrent neural networks, gated architectures such as the GRU have contributed to the development of highly accurate machine learning models that can tackle long-term dependencies in the data. However, the training of such networks is performed by the expensive algorithm of gradient descent with backpropagation through time. On the other hand, reservoir computing approaches such as Echo State Networks (ESNs) can produce models that can be trained efficiently thanks to the use of fixed random parameters, but are not ideal for dealing with data presenting long-term dependencies. We explore the problem of employing gated architectures in ESNs from both theoretical and empirical perspectives. We do so by deriving and evaluating a necessary condition for the non-contractivity of the state transition function, which is important to overcome the fading-memory characterization of conventional ESNs. We find that using pure reservoir computing methodologies is not sufficient for effective gating mechanisms, while instead training even only the gates is highly effective in terms of predictive accuracy.

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Mastoi, Qurat-ul-ain, Teh Wah, and Ram Gopal Raj. "Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification." Applied Sciences 9, no. 4 (2019): 702. http://dx.doi.org/10.3390/app9040702.

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The abnormal conduction of cardiac activity in the lower chamber of the heart (ventricular) can cause cardiac diseases and sometimes leads to sudden death. In this paper, the author proposed the Reservoir Computing (RC) based Echo State Networks (ESNs) for ventricular heartbeat classification based on a single Electrocardiogram (ECG) lead. The Association for the Advancement of Medical Instrumentation (AAMI) standards were used to preprocesses the standardized diagnostic tool (ECG signals) based on the interpatient scheme. Despite the extensive efforts and notable experiments that have been done on machine learning techniques for heartbeat classification, ESNs are yet to be considered for heartbeat classification as a is fast, scalable, and reliable approach for real-time scenarios. Our proposed method was especially designed for Medical Internet of Things (MIoT) devices, for instance wearable wireless devices for ECG monitoring or ventricular heart beat detection systems and so on. The experiments were conducted on two public datasets, namely AHA and MIT-BIH-SVDM. The performance of the proposed model was evaluated using the MIT-BIH-AR dataset and it achieved remarkable results. The positive predictive value and sensitivity are 98.98% and 98.98%, respectively for the modified lead II (MLII) and 98.96% and 97.95 for the V1 lead, respectively. However, the experimental results of the state-of-the-art approaches, namely the patient-adaptable method, improved generalization, and the multiview learning approach obtained 92.8%, 87.0%, and 98.0% positive predictive values, respectively. These obtained results of the existing studies exemplify that the performance of this method achieved higher accuracy. We believe that the improved classification accuracy opens up the possibility for implementation of this methodology in Medical Internet of Things (MIoT) devices in order to bring improvements in e-health systems.

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Mu, Xiaohui, Lixiang Li, and Xiangyu He. "Research on Sparsity of Output Synapses in Echo State Networks." Mathematical Problems in Engineering 2018 (December 18, 2018): 1–12. http://dx.doi.org/10.1155/2018/1984524.

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This paper presents an improved model of echo state networks (ESNs) and gives the definitions of energy consumption, energy efficiency, etc. We verify the existence of redundant output synaptic connections by numerical simulations. We investigate the relationships among energy consumption, prediction step, and the sparsity of ESN. At the same time, the energy efficiency and the prediction steps are found to present the same variation trend when silencing different synapses. Thus, we propose a computationally efficient method to locate redundant output synapses based on energy efficiency of ESN. We find that the neuron states of redundant synapses can be linearly represented by the states of other neurons. We investigate the contributions of redundant and core output synapses to the performance of network prediction. For the prediction task of chaotic time series, the predictive performance of ESN is improved about hundreds of steps by silencing redundant synapses.

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Batu, Barın,. "Investigating Performance of ESN’s in Forecasting Financial Metrics When Compared To Traditional RNN Types." International Journal of Social Science and Economic Research 09, no. 06 (2024): 1950–82. http://dx.doi.org/10.46609/ijsser.2024.v09i06.023.

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This research investigates the performance of Echo State Networks (ESN) in forecasting financial metrics and compares their effectiveness against traditional recurrent neural network (RNN) architectures like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU), as well as Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) models. By analyzing datasets sourced from Yahoo Finance for various financial indices, exchange-traded funds and stocks over five years, this study examines the accuracy, and structural simplicity of ESNs in predicting close prices, daily volatility, and log returns. Results indicate that ESNs, with their reservoir computing capabilities, outperform traditional RNNs by achieving lower mean absolute error (MAE) and mean squared error (MSE) overall, highlighting their potential as efficient and robust tools for financial time-series forecasting.

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Iacob, Stefan, and Joni Dambre. "Memory–Non-Linearity Trade-Off in Distance-Based Delay Networks." Biomimetics 9, no. 12 (2024): 755. https://doi.org/10.3390/biomimetics9120755.

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The performance of echo state networks (ESNs) in temporal pattern learning tasks depends both on their memory capacity (MC) and their non-linear processing. It has been shown that linear memory capacity is maximized when ESN neurons have linear activation, and that a trade-off between non-linearity and linear memory capacity is required for temporal pattern learning tasks. The more recent distance-based delay networks (DDNs) have shown improved memory capacity over ESNs in several benchmark temporal pattern learning tasks. However, it has not thus far been studied whether this increased memory capacity comes at the cost of reduced non-linear processing. In this paper, we advance the hypothesis that DDNs in fact achieve a better trade-off between linear MC and non-linearity than ESNs, by showing that DDNs can have strong non-linearity with large memory spans. We tested this hypothesis using the NARMA-30 task and the bitwise delayed XOR task, two commonly used reservoir benchmark tasks that require a high degree of both non-linearity and memory.

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Iacob, Stefan, and Joni Dambre. "Exploiting Signal Propagation Delays to Match Task Memory Requirements in Reservoir Computing." Biomimetics 9, no. 6 (2024): 355. http://dx.doi.org/10.3390/biomimetics9060355.

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Recurrent neural networks (RNNs) transmit information over time through recurrent connections. In contrast, biological neural networks use many other temporal processing mechanisms. One of these mechanisms is the inter-neuron delays caused by varying axon properties. Recently, this feature was implemented in echo state networks (ESNs), a type of RNN, by assigning spatial locations to neurons and introducing distance-dependent inter-neuron delays. These delays were shown to significantly improve ESN task performance. However, thus far, it is still unclear why distance-based delay networks (DDNs) perform better than ESNs. In this paper, we show that by optimizing inter-node delays, the memory capacity of the network matches the memory requirements of the task. As such, networks concentrate their memory capabilities to the points in the past which contain the most information for the task at hand. Moreover, we show that DDNs have a greater total linear memory capacity, with the same amount of non-linear processing power.

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Lee, Gin Chong, Chu Kiong Loo, and Wei Shiung Liew. "SELF-ORGANIZING RESERVOIR NETWORK FOR ACTION RECOGNITION." Malaysian Journal of Computer Science 35, no. 3 (2022): 243–63. http://dx.doi.org/10.22452/mjcs.vol35no3.4.

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Current research in human action recognition (HAR) focuses on efficient and effective modelling of the temporal features of human actions in 3-dimensional space. Echo State Networks (ESNs) are one suitable method for encoding the temporal context due to its short-term memory property. However, the random initialization of the ESN's input and reservoir weights may increase instability and variance in generalization. Inspired by the notion that input-dependent self-organization is decisive for the cortex to adjust the neurons according to the distribution of the inputs, a Self-Organizing Reservoir Network (SORN) is developed based on Adaptive Resonance Theory (ART) and Instantaneous Topological Mapping (ITM) as the clustering process to cater deterministic initialization of the ESN reservoirs in a Convolutional Echo State Network (ConvESN) and yield a Self-Organizing Convolutional Echo State Network (SO-ConvESN). SORN ensures that the activation of ESN’s internal echo state representations reflects similar topological qualities of the input signal which should yield a self-organizing reservoir. In the context of HAR task, human actions encoded as a multivariate time series signals are clustered into clustered node centroids and interconnectivity matrices by SORN for initializing the SO-ConvESN reservoirs. By using several publicly available 3D-skeleton-based action recognition datasets, the impact of vigilance threshold and reservoir perturbation of SORN in performing clustering, the SORN reservoir dynamics and the capability of SO-ConvESN on HAR task have been empirically evaluated and analyzed to produce competitive experimental results.

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Martinuzzi, Francesco, Miguel D. Mahecha, Gustau Camps-Valls, David Montero, Tristan Williams, and Karin Mora. "Learning extreme vegetation response to climate drivers with recurrent neural networks." Nonlinear Processes in Geophysics 31, no. 4 (2024): 535–57. http://dx.doi.org/10.5194/npg-31-535-2024.

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Abstract. The spectral signatures of vegetation are indicative of ecosystem states and health. Spectral indices used to monitor vegetation are characterized by long-term trends, seasonal fluctuations, and responses to weather anomalies. This study investigates the potential of neural networks in learning and predicting vegetation response, including extreme behavior from meteorological data. While machine learning methods, particularly neural networks, have significantly advanced in modeling nonlinear dynamics, it has become standard practice to approach the problem using recurrent architectures capable of capturing nonlinear effects and accommodating both long- and short-term memory. We compare four recurrent-based learning models, which differ in their training and architecture for predicting spectral indices at different forest sites in Europe: (1) recurrent neural networks (RNNs), (2) long short-term memory networks (LSTMs), (3) gated recurrent unit networks (GRUs), and (4) echo state networks (ESNs). While our results show minimal quantitative differences in their performances, ESNs exhibit slightly superior results across various metrics. Overall, we show that recurrent network architectures prove generally suitable for vegetation state prediction yet exhibit limitations under extreme conditions. This study highlights the potential of recurrent network architectures for vegetation state prediction, emphasizing the need for further research to address limitations in modeling extreme conditions within ecosystem dynamics.

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Lun, Shuxian, Qian Wang, Jianning Cai, and Xiaodong Lu. "A Multireservoir Echo State Network Combined with Olfactory Feelings Structure." Electronics 12, no. 22 (2023): 4635. http://dx.doi.org/10.3390/electronics12224635.

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As a special form of recurrent neural network (RNN), echo state networks (ESNs) have achieved good results in nonlinear system modeling, fuzzy nonlinear control, time series prediction, and so on. However, the traditional single-reservoir ESN topology limits the prediction ability of the network. In this paper, we design a multireservoir olfactory feelings echo state network (OFESN) inspired by the structure of the Drosophila olfactory bulb, which provides a new connection mode. The connection between subreservoirs is transformed into the connection between each autonomous neuron, the neurons in each subreservoir are sparsely connected, and the neurons in different subreservoirs cannot communicate with each other. The OFESN greatly simplifies the coupling connections between neurons in different libraries, reduces information redundancy, and improves the running speed of the network. The findings from the simulation demonstrate that the OFESN model, as introduced in this study, enhances the capacity to approximate sine superposition function and the Mackey–Glass system when combined. Additionally, this model exhibits improved prediction accuracy by 98% in some cases and reduced fluctuations in prediction errors.

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Mienye, Ibomoiye Domor, Theo G. Swart, and George Obaido. "Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications." Information 15, no. 9 (2024): 517. http://dx.doi.org/10.3390/info15090517.

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Recurrent neural networks (RNNs) have significantly advanced the field of machine learning (ML) by enabling the effective processing of sequential data. This paper provides a comprehensive review of RNNs and their applications, highlighting advancements in architectures, such as long short-term memory (LSTM) networks, gated recurrent units (GRUs), bidirectional LSTM (BiLSTM), echo state networks (ESNs), peephole LSTM, and stacked LSTM. The study examines the application of RNNs to different domains, including natural language processing (NLP), speech recognition, time series forecasting, autonomous vehicles, and anomaly detection. Additionally, the study discusses recent innovations, such as the integration of attention mechanisms and the development of hybrid models that combine RNNs with convolutional neural networks (CNNs) and transformer architectures. This review aims to provide ML researchers and practitioners with a comprehensive overview of the current state and future directions of RNN research.

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Fazzini, Paolo, Marco Montuori, Antonello Pasini, et al. "Forecasting PM10 Levels Using Machine Learning Models in the Arctic: A Comparative Study." Remote Sensing 15, no. 13 (2023): 3348. http://dx.doi.org/10.3390/rs15133348.

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In this study, we present a statistical forecasting framework and assess its efficacy using a range of established machine learning algorithms for predicting Particulate Matter (PM) concentrations in the Arctic, specifically in Pallas (FI), Reykjavik (IS), and Tromso (NO). Our framework leverages historical ground measurements and 24 h predictions from nine models by the Copernicus Atmosphere Monitoring Service (CAMS) to provide PM10 predictions for the following 24 h. Furthermore, we compare the performance of various memory cells based on artificial neural networks (ANN), including recurrent neural networks (RNNs), gated recurrent units (GRUs), long short-term memory networks (LSTMs), echo state networks (ESNs), and windowed multilayer perceptrons (MLPs). Regardless of the type of memory cell chosen, our results consistently show that the proposed framework outperforms the CAMS models in terms of mean squared error (MSE), with average improvements ranging from 25% to 40%. Furthermore, we examine the impact of outliers on the overall performance of the model.

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Andrecut, M. "Reservoir computing on the hypersphere." International Journal of Modern Physics C 28, no. 07 (2017): 1750095. http://dx.doi.org/10.1142/s0129183117500954.

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Reservoir Computing (RC) refers to a Recurrent Neural Network (RNNs) framework, frequently used for sequence learning and time series prediction. The RC system consists of a random fixed-weight RNN (the input-hidden reservoir layer) and a classifier (the hidden-output readout layer). Here, we focus on the sequence learning problem, and we explore a different approach to RC. More specifically, we remove the nonlinear neural activation function, and we consider an orthogonal reservoir acting on normalized states on the unit hypersphere. Surprisingly, our numerical results show that the system’s memory capacity exceeds the dimensionality of the reservoir, which is the upper bound for the typical RC approach based on Echo State Networks (ESNs). We also show how the proposed system can be applied to symmetric cryptography problems, and we include a numerical implementation.

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López-Ortiz, E. J., M. Perea-Trigo, L. M. Soria-Morillo, J. A. Álvarez-García, and J. J. Vegas-Olmos. "Energy-Efficient Edge and Cloud Image Classification with Multi-Reservoir Echo State Network and Data Processing Units." Sensors 24, no. 11 (2024): 3640. http://dx.doi.org/10.3390/s24113640.

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In an era dominated by Internet of Things (IoT) devices, software-as-a-service (SaaS) platforms, and rapid advances in cloud and edge computing, the demand for efficient and lightweight models suitable for resource-constrained devices such as data processing units (DPUs) has surged. Traditional deep learning models, such as convolutional neural networks (CNNs), pose significant computational and memory challenges, limiting their use in resource-constrained environments. Echo State Networks (ESNs), based on reservoir computing principles, offer a promising alternative with reduced computational complexity and shorter training times. This study explores the applicability of ESN-based architectures in image classification and weather forecasting tasks, using benchmarks such as the MNIST, FashionMnist, and CloudCast datasets. Through comprehensive evaluations, the Multi-Reservoir ESN (MRESN) architecture emerges as a standout performer, demonstrating its potential for deployment on DPUs or home stations. In exploiting the dynamic adaptability of MRESN to changing input signals, such as weather forecasts, continuous on-device training becomes feasible, eliminating the need for static pre-trained models. Our results highlight the importance of lightweight models such as MRESN in cloud and edge computing applications where efficiency and sustainability are paramount. This study contributes to the advancement of efficient computing practices by providing novel insights into the performance and versatility of MRESN architectures. By facilitating the adoption of lightweight models in resource-constrained environments, our research provides a viable alternative for improved efficiency and scalability in modern computing paradigms.

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OZTURK, MUSTAFA C., and JOSE C. PRINCIPE. "FREEMAN'S K MODELS AS RESERVOIR COMPUTING ARCHITECTURES." New Mathematics and Natural Computation 05, no. 01 (2009): 265–86. http://dx.doi.org/10.1142/s179300570900126x.

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Walter Freeman in his classic 1975 book "Mass Activation of the Nervous System" presented a hierarchy of dynamical computational models based on studies and measurements done in real brains, which has been known as the Freeman's K model (FKM). Much more recently, liquid state machine (LSM) and echo state network (ESN) have been proposed as universal approximators in the class of functionals with exponential decaying memory. In this paper, we briefly review these models and show that the restricted K set architecture of KI and KII networks share the same properties of LSM/ESNs and is therefore one more member of the reservoir computing family. In the reservoir computing perspective, the states of the FKM are a representation space that stores in its spatio-temporal dynamics a short-term history of the input patterns. Then at any time, with a simple instantaneous read-out made up of a KI, information related to the input history can be accessed and read out. This work provides two important contributions. First, it emphasizes the need for optimal readouts, and shows how to adaptively design them. Second, it shows that the Freeman model is able to process continuous signals with temporal structure. We will provide theoretical results for the conditions on the system parameters of FKM satisfying the echo state property. Experimental results are presented to illustrate the validity of the proposed approach.

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Zeng, Xiao, Jing Li, Pengcheng Yang, Hongda Cai, Yongzhi Zhou, and Daren Li. "An Echo State Network Approach for Parameter Variation Robustness Enhancement in FCS-MPC for PMSM Drives." Applied Sciences 15, no. 11 (2025): 6288. https://doi.org/10.3390/app15116288.

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Parameter mismatch in model predictive control (MPC) strategies presents significant challenge in permanent magnet synchronous motor (PMSM) control, often leading to reduced tracking accuracy and compromised system stability under dynamic operating conditions. To address above issue, this article proposes a modified parameter robust FCS-MPC framework that integrates an online learning echo state network (ESN) for real-time compensation of parameter deviations. By leveraging the structural simplicity and application efficiency of ESNs during training, the proposed approach is well-suited to tackling complex parameter variation challenges via online learning. Initially, the ESN is trained offline using data derived from a PMSM-MPC control environment. Subsequently, the trained ESN replaces the predictive model of the MPC controller, enabling online learning under varying PMSM driving conditions. The incorporation of an online ESN allows the proposed controller to achieve real-time adjustments that mitigate the effects of parameter mismatch. Plenty of simulation studies are available and demonstrate that the proposed ESN-MPC controller exhibits enhanced robustness against parameter mismatch compared to the traditional FCS-MPC method.

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Khan, Imran Ullah, and Jong Weon Lee. "PAR-Net: An Enhanced Dual-Stream CNN–ESN Architecture for Human Physical Activity Recognition." Sensors 24, no. 6 (2024): 1908. http://dx.doi.org/10.3390/s24061908.

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Physical exercise affects many facets of life, including mental health, social interaction, physical fitness, and illness prevention, among many others. Therefore, several AI-driven techniques have been developed in the literature to recognize human physical activities. However, these techniques fail to adequately learn the temporal and spatial features of the data patterns. Additionally, these techniques are unable to fully comprehend complex activity patterns over different periods, emphasizing the need for enhanced architectures to further increase accuracy by learning spatiotemporal dependencies in the data individually. Therefore, in this work, we develop an attention-enhanced dual-stream network (PAR-Net) for physical activity recognition with the ability to extract both spatial and temporal features simultaneously. The PAR-Net integrates convolutional neural networks (CNNs) and echo state networks (ESNs), followed by a self-attention mechanism for optimal feature selection. The dual-stream feature extraction mechanism enables the PAR-Net to learn spatiotemporal dependencies from actual data. Furthermore, the incorporation of a self-attention mechanism makes a substantial contribution by facilitating targeted attention on significant features, hence enhancing the identification of nuanced activity patterns. The PAR-Net was evaluated on two benchmark physical activity recognition datasets and achieved higher performance by surpassing the baselines comparatively. Additionally, a thorough ablation study was conducted to determine the best optimal model for human physical activity recognition.

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Gao, Yunzhu, Jun Wang, Lin Guo, and Hong Peng. "Short-Term Photovoltaic Power Prediction Using Nonlinear Spiking Neural P Systems." Sustainability 16, no. 4 (2024): 1709. http://dx.doi.org/10.3390/su16041709.

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To ensure high-quality electricity, improve the dependability of power systems, reduce carbon emissions, and promote the sustainable development of clean energy, short-term photovoltaic (PV) power prediction is crucial. However, PV power is highly stochastic and volatile, making accurate predictions of PV power very difficult. To address this challenging prediction problem, in this paper, a novel method to predict the short-term PV power using a nonlinear spiking neural P system-based ESN model has been proposed. First, we combine a nonlinear spiking neural P (NSNP) system with a neural-like computational model, enabling it to effectively capture the complex nonlinear trends in PV sequences. Furthermore, an NSNP system featuring a layer is designed. Input weights and NSNP reservoir weights are randomly initialized in the proposed model, while the output weights are trained by the Ridge Regression algorithm, which is motivated by the learning mechanism of echo state networks (ESNs), providing the model with an adaptability to complex nonlinear trends in PV sequences and granting it greater flexibility. Three case studies are conducted on real datasets from Alice Springs, Australia, comparing the proposed model with 11 baseline models. The outcomes of the experiments exhibit that the model performs well in tasks of PV power prediction.

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Bouazizi, Samar, Emna Benmohamed, and Hela Ltifi. "Enhancing EEG-based emotion recognition using PSD-Grouped Deep Echo State Network." JUCS - Journal of Universal Computer Science 29, no. (10) (2023): 1116–38. https://doi.org/10.3897/jucs.98789.

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Emotions are a crucial aspect of daily life and play a vital role in shaping human inter-actions. The purpose of this paper is to introduce a novel approach to recognize human emotions through the use of electroencephalogram (EEG) signals. To recognize these signals for emotion prediction, we employ a paradigm of Reservoir Computing (RC), called Echo State Network (ESN). In our analysis, we focus on two specific classes of emotion recognition: H/L Arousal and H/L Valence. We suggest using the Deep ESN model in conjunction with the Welch Power Spectral Density (Wlech PSD) method for emotion classification and feature extraction. Furthermore, we feed the selected features to a grouped ESN for recognizing emotions. Our approach is validated on the well-known DEAP benchmark, which includes the EEG data from 32 participants. The proposed model achieved 89.32% accuracy for H/L Arousal and 91.21% accuracy for H/L Valence on the DEAP dataset. The obtained results demonstrate the effectiveness of our approach, which yields good performance compared to existing models of emotion analysis based on EEG.

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Suntaxi, Gabriela. "Editorial of Number 2 Volume 11 of Latin-American Journal of Computing." Latin-American Journal of Computing 11, no. 2 (2024): 8–10. https://doi.org/10.5281/zenodo.12168733.

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We are pleased to share Volume 11, Issue 2 of the Latin American Journal of Computing (LAJC) with you. This edition includes a selection of pioneering research articles that demonstrate the latest advancements in the computer science field. Each paper included in this volume represents rigorous academic research and innovative problem-solving methods. We believe that the insights and discoveries presented here will significantly contribute to the field, stimulate insightful discussions, and inspire future innovations. This issue begins with three articles that explore advanced methodologies in process monitoring, heat transfer, and robotics. The first article investigates the use of Echo State Networks (ESNs) to create digital twins for nonlinear dynamic chemical processes, demonstrating the potential of ESNs in generating efficient surrogate models for real-time process monitoring and control. The second article addresses the inverse problem in heat transfer modeling using the Transition Markov Chain Monte Carlo method, showcasing its effectiveness in estimating spatially variable thermophysical properties. Next, Janarthanan et al. explore the potential of data generated by robots, specifically focusing on ROS Bag files used in the Robot Operating System (ROS). The study highlights security concerns, such as unauthorized access and data theft, due to plain text communication in legacy ROS systems.  This issue also delves into the critical applications of artificial intelligence and machine learning in various scientific and industrial domains. The fourth article presents the ANN-MoC approach for solving inverse transient transport problems, showcasing its potential in engineering and medical fields by accurately estimating absorption coefficients from scalar flux measurements. Next, another study explores the impact of data balance on short-term rainfall forecasts using Artificial Neural Networks (ANNs) with data from the Amazon Tall Tower Observatory (ATTO). This research emphasizes the necessity of balanced data to improve the accuracy and reliability of meteorological models, highlighting the broader implications for environmental monitoring and prediction. Additionally, the volume includes an innovative fault classification model for industrial processes, merging Decision Trees with Genetic Programming to enhance preventive and corrective measures.  Finally, we explore financial markets and technological advancements. One article compares the Brazilian stock market with cryptocurrencies like Bitcoin, Ethereum, and Solana, using the Kolmogorov-Smirnov test to examine their relationships and potential investment opportunities. The last study uses machine learning and the Grey Wolf Optimization meta-heuristic to predict Brazil's electricity demand, showcasing advanced regression models for accurate energy consumption forecasting.  We hope that the diverse range of topics and innovative approaches presented in this volume will inspire your own research endeavors. The advancements in computational intelligence, machine learning, and data analysis showcased here underscore the transformative potential of these technologies in addressing real-world challenges. As we continue to explore the frontiers of computer science, we invite you to join us in pushing the boundaries of knowledge within our scientific community. Together, we can drive progress and make meaningful contributions to the field.   Gabriela Suntaxi Editor-in-Chief

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Yamashita, Kodai, and Tomoki Hamagami. "Reinforcement Learning for POMDP Environments Using State Representation with Reservoir Computing." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 4 (2022): 562–69. http://dx.doi.org/10.20965/jaciii.2022.p0562.

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One of the challenges in reinforcement learning is regarding the partially observable Markov decision process (POMDP). In this case, an agent cannot observe the true state of the environment and perceive different states to be the same. Our proposed method uses the agent’s time-series information to deal with this imperfect perception problem. In particular, the proposed method uses reservoir computing to transform the time-series of observation information into a non-linear state. A typical model of reservoir computing, the echo state network (ESN), transforms raw observations into reservoir states. The proposed method is named dual ESNs reinforcement learning, which uses two ESNs specialized for observation and action information. The experimental results show the effectiveness of the proposed method in environments where imperfect perception problems occur.

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Dan, Jingpei, Wenbo Guo, Weiren Shi, Bin Fang, and Tingping Zhang. "PSO Based Deterministic ESN Models for Stock Price Forecasting." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 2 (2015): 312–18. http://dx.doi.org/10.20965/jaciii.2015.p0312.

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Deterministic echo state network (ESN) models integrated with particle swarm optimization (PSO) are proposed to improve the accuracy and efficiency of stock price forecasting. ESNs have been successfully applied to financial time series forecasting because of their efficient and powerful computational ability in approximating nonlinear dynamical systems. However, reservoir construction in standard ESNs is primarily driven by a series of randomized model-building stages, because of which both researchers and practitioners have to rely on a series of trials and errors to determine parameters. An ESN with a deterministically constructed reservoir is comparable in performance to a standard ESN and has minimal complexity as well as potential for optimizations with regard to ESN parameters. In this paper, forecasting performances of the proposed PSO-DESN models are compared with those of standard ESNs for stock price prediction on the benchmark dataset of S&P 500. The comparison results demonstrate that the proposed PSO-DESNs exhibit better performance in stock price forecasting in terms of both accuracy and efficiency, thereby verifying the potential of PSO-DESNs for financial predictions.

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Koryakin, Danil, Johannes Lohmann, and Martin V. Butz. "Balanced echo state networks." Neural Networks 36 (December 2012): 35–45. http://dx.doi.org/10.1016/j.neunet.2012.08.008.

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Gallicchio, Claudio, and Alessio Micheli. "Tree Echo State Networks." Neurocomputing 101 (February 2013): 319–37. http://dx.doi.org/10.1016/j.neucom.2012.08.017.

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Lymburn, Thomas, Alexander Khor, Thomas Stemler, Débora C. Corrêa, Michael Small, and Thomas Jüngling. "Consistency in echo-state networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 29, no. 2 (2019): 023118. http://dx.doi.org/10.1063/1.5079686.

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Grigoryeva, Lyudmila, and Juan-Pablo Ortega. "Echo state networks are universal." Neural Networks 108 (December 2018): 495–508. http://dx.doi.org/10.1016/j.neunet.2018.08.025.

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Doan, N. A. K., W. Polifke, and L. Magri. "Physics-informed echo state networks." Journal of Computational Science 47 (November 2020): 101237. http://dx.doi.org/10.1016/j.jocs.2020.101237.

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Yili Xia, Cyrus Jahanchahi, and Danilo P. Mandic. "Quaternion-Valued Echo State Networks." IEEE Transactions on Neural Networks and Learning Systems 26, no. 4 (2015): 663–73. http://dx.doi.org/10.1109/tnnls.2014.2320715.

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Pyle, Ryan, Nikola Jovanovic, Devika Subramanian, Krishna V. Palem, and Ankit B. Patel. "Domain-driven models yield better predictions at lower cost than reservoir computers in Lorenz systems." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2194 (2021): 20200246. http://dx.doi.org/10.1098/rsta.2020.0246.

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Recent advances in computing algorithms and hardware have rekindled interest in developing high-accuracy, low-cost surrogate models for simulating physical systems. The idea is to replace expensive numerical integration of complex coupled partial differential equations at fine time scales performed on supercomputers, with machine-learned surrogates that efficiently and accurately forecast future system states using data sampled from the underlying system. One particularly popular technique being explored within the weather and climate modelling community is the echo state network (ESN), an attractive alternative to other well-known deep learning architectures. Using the classical Lorenz 63 system, and the three tier multi-scale Lorenz 96 system (Thornes T, Duben P, Palmer T. 2017 Q. J. R. Meteorol. Soc. 143 , 897–908. ( doi:10.1002/qj.2974 )) as benchmarks, we realize that previously studied state-of-the-art ESNs operate in two distinct regimes, corresponding to low and high spectral radius (LSR/HSR) for the sparse, randomly generated, reservoir recurrence matrix. Using knowledge of the mathematical structure of the Lorenz systems along with systematic ablation and hyperparameter sensitivity analyses, we show that state-of-the-art LSR-ESNs reduce to a polynomial regression model which we call Domain-Driven Regularized Regression (D2R2). Interestingly, D2R2 is a generalization of the well-known SINDy algorithm (Brunton SL, Proctor JL, Kutz JN. 2016 Proc. Natl Acad. Sci. USA 113 , 3932–3937. ( doi:10.1073/pnas.1517384113 )). We also show experimentally that LSR-ESNs (Chattopadhyay A, Hassanzadeh P, Subramanian D. 2019 ( http://arxiv.org/abs/1906.08829 )) outperform HSR ESNs (Pathak J, Hunt B, Girvan M, Lu Z, Ott E. 2018 Phys. Rev. Lett. 120 , 024102. ( doi:10.1103/PhysRevLett.120.024102 )) while D2R2 dominates both approaches. A significant goal in constructing surrogates is to cope with barriers to scaling in weather prediction and simulation of dynamical systems that are imposed by time and energy consumption in supercomputers. Inexact computing has emerged as a novel approach to helping with scaling. In this paper, we evaluate the performance of three models (LSR-ESN, HSR-ESN and D2R2) by varying the precision or word size of the computation as our inexactness-controlling parameter. For precisions of 64, 32 and 16 bits, we show that, surprisingly, the least expensive D2R2 method yields the most robust results and the greatest savings compared to ESNs. Specifically, D2R2 achieves 68 × in computational savings, with an additional 2 × if precision reductions are also employed, outperforming ESN variants by a large margin. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

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Ashour, Wesam M., Abdallatif S. Abu-Issa, and Olaf Hellwich. "Clustering Algorithms in Echo State Networks." International Journal of Signal Processing, Image Processing and Pattern Recognition 9, no. 5 (2013): 15–24. http://dx.doi.org/10.14257/ijsip.2016.9.5.02.

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Gallicchio, Claudio, Alessio Micheli, and Luca Pedrelli. "Design of deep echo state networks." Neural Networks 108 (December 2018): 33–47. http://dx.doi.org/10.1016/j.neunet.2018.08.002.

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Chen, Xueliang, Weimin Zhong, Xin Peng, Peihao Du, and Zhongmei Li. "An Improved Adaptive Dynamic Programming Algorithm Based on Fuzzy Extended State Observer for Dissolved Oxygen Concentration Control." Processes 10, no. 12 (2022): 2618. http://dx.doi.org/10.3390/pr10122618.

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To solve the anti-disturbance control problem of dissolved oxygen concentration in the wastewater treatment plant (WWTP), an anti-disturbance control scheme based on reinforcement learning (RL) is proposed. An extended state observer (ESO) based on the Takagi–Sugeno (T-S) fuzzy model is first designed to estimate the the system state and total disturbance. The anti-disturbance controller compensates for the total disturbance based on the output of the observer in real time, online searches the optimal control policy using a neural-network-based adaptive dynamic programming (ADP) controller. For reducing the computational complexity and avoiding local optimal solutions, the echo state network (ESN) is used to approximate the optimal control policy and optimal value function in the ADP controller. Further analysis demonstrates the observer estimation errors for system state and total disturbance are bounded, and the weights of ESNs in the ADP controller are convergent. Finally, the effectiveness of the proposed ESO-based ADP control scheme is evaluated on a benchmark simulation model of the WWTP.

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Gonon, Lukas, and Juan-Pablo Ortega. "Fading memory echo state networks are universal." Neural Networks 138 (June 2021): 10–13. http://dx.doi.org/10.1016/j.neunet.2021.01.025.

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Goudarzi, Alireza, and Darko Stefanovic. "Towards a Calculus of Echo State Networks." Procedia Computer Science 41 (2014): 176–81. http://dx.doi.org/10.1016/j.procs.2014.11.101.

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Tong, Matthew H., Adam D. Bickett, Eric M. Christiansen, and Garrison W. Cottrell. "Learning grammatical structure with Echo State Networks." Neural Networks 20, no. 3 (2007): 424–32. http://dx.doi.org/10.1016/j.neunet.2007.04.013.

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Xue, Yanbo, Le Yang, and Simon Haykin. "Decoupled echo state networks with lateral inhibition." Neural Networks 20, no. 3 (2007): 365–76. http://dx.doi.org/10.1016/j.neunet.2007.04.014.

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Aceituno, Pau Vilimelis, Gang Yan, and Yang-Yu Liu. "Tailoring Echo State Networks for Optimal Learning." iScience 23, no. 9 (2020): 101440. http://dx.doi.org/10.1016/j.isci.2020.101440.

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de Vos, N. J. "Echo state networks as an alternative to traditional artificial neural networks in rainfall–runoff modelling." Hydrology and Earth System Sciences 17, no. 1 (2013): 253–67. http://dx.doi.org/10.5194/hess-17-253-2013.

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Abstract. Despite theoretical benefits of recurrent artificial neural networks over their feedforward counterparts, it is still unclear whether the former offer practical advantages as rainfall–runoff models. The main drawback of recurrent networks is the increased complexity of the training procedure due to their architecture. This work uses the recently introduced and conceptually simple echo state networks for streamflow forecasts on twelve river basins in the Eastern United States, and compares them to a variety of traditional feedforward and recurrent approaches. Two modifications on the echo state network models are made that increase the hydrologically relevant information content of their internal state. The results show that the echo state networks outperform feedforward networks and are competitive with state-of-the-art recurrent networks, across a range of performance measures. This, along with their simplicity and ease of training, suggests that they can be considered promising alternatives to traditional artificial neural networks in rainfall–runoff modelling.

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Merlin Mathew, Rincy, S. Purushothaman, and P. Rajeswari. "Performance comparisons of particle swarm optimization, echo state neural network and genetic algorithm for vegetation segmentation." International Journal of Engineering & Technology 7, no. 1.1 (2017): 184. http://dx.doi.org/10.14419/ijet.v7i1.1.9286.

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This article presents the implementation of vegetation segmentation by using soft computing methods: particle swarm optimization (PSO), echostate neural network(ESNN) and genetic algorithm (GA). Multispectral image with the required band from Landsat 8 (5, 4, 3) and Landsat 7 (4, 3, 2) are used. In this paper, images from ERDAS format acquired by Landsat 7 ‘Paris.lan’ (band 4, band 3, Band 2) and image acquired from Landsat 8 (band5, band 4, band 3) are used. The soft computing algorithms are used to segment the plane-1(Near infra-red spectra) and plane 2(RED spectra). The monochrome of the two segmented images is compared to present performance comparisons of the implemented algorithms.

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Sun, Jingyu, Lixiang Li, and Haipeng Peng. "Sequence Prediction and Classification of Echo State Networks." Mathematics 11, no. 22 (2023): 4640. http://dx.doi.org/10.3390/math11224640.

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The echo state network is a unique form of recurrent neural network. Due to its feedback mechanism, it exhibits superior nonlinear behavior compared to traditional neural networks and is highly regarded for its simplicity and efficiency in computation. In recent years, as network development has progressed, the security threats faced by networks have increased. To detect and counter these threats, the analysis of network traffic has become a crucial research focus. The echo state network has demonstrated exceptional performance in sequence prediction. In this article, we delve into the impact of echo state networks on time series. We have enhanced the model by increasing the number of layers and adopting a different data input approach. We apply it to predict chaotic systems that appear ostensibly regular but are inherently irregular. Additionally, we utilize it for the classification of sound sequence data. Upon evaluating the model using root mean squared error and micro-F1, we have observed that our model exhibits commendable accuracy and stability.

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Jaeger, Herbert, Wolfgang Maass, and Jose Principe. "Special issue on echo state networks and liquid state machines." Neural Networks 20, no. 3 (2007): 287–89. http://dx.doi.org/10.1016/j.neunet.2007.04.001.

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Pei, Jiaxin, and Jian Wang. "Multisensor Prognostic of RUL Based on EMD-ESN." Mathematical Problems in Engineering 2020 (November 24, 2020): 1–12. http://dx.doi.org/10.1155/2020/6639171.

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This paper presents a prognostic method for RUL (remaining useful life) prediction based on EMD (empirical mode decomposition)-ESN (echo state network). The combination method adopts EMD to decompose the multisensor time series into a bunch of IMFs (intrinsic mode functions), which are then predicted by ESNs, and the outputs of each ESN are summarized to obtain the final prediction value. The EMD can decompose the original data into simpler portions and during the decomposition process, much noise is filtered out and the subsequent prediction is much easier. The ESN is a relatively new type of RNN (recurrent neural network), which substitutes the hidden layers with a reservoir remaining unchanged during the training phase. The characteristic makes the training time of ESN is much shorter than traditional RNN. The proposed method is applied to the turbofan engine datasets and is compared with LSTM (Long Short-Term Memory) and ESN. Extensive experimental results show that the prediction performance and efficiency are much improved by the proposed method.

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VLAD, Sorin, and Ionel GORDIN. "Echo State Networks for predicting financial time series." Journal of Applied Computer Science & Mathematics 15, no. 2 (2021): 44–48. http://dx.doi.org/10.4316/jacsm.202102006.

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Ariizumi, Ryo, Masanori Kawaguchi, Toshiya Arakawa, Naoya Oue, and Masaru Murayama. "Drowsiness Estimation of Drivers Using Echo State Networks." International Journal of Automotive Engineering 13, no. 2 (2022): 60–67. http://dx.doi.org/10.20485/jsaeijae.13.2_60.

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Ozdemir, Anil, Mark Scerri, Andrew B. Barron, et al. "EchoVPR: Echo State Networks for Visual Place Recognition." IEEE Robotics and Automation Letters 7, no. 2 (2022): 4520–27. http://dx.doi.org/10.1109/lra.2022.3150505.

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Sun, Xiaochuan. "A Wavelet Perspective on Deterministic Echo State Networks." Journal of Information and Computational Science 12, no. 4 (2015): 1639–46. http://dx.doi.org/10.12733/jics20105486.

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