Prediction of chaotic time series using recurrent neural networks and reservoir computing techniques: A comparative study

Shahi, Shahrokh; Fenton, Flavio H.; Cherry, Elizabeth M.

doi:10.1016/j.mlwa.2022.100300

Cited by 40 publications

(17 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Spine in the picture shown has four segments with three actuator, these actuator will generate force to balance the center of mass into the dynamic surface, 2, 8 this spine figure is the internal structure, it will wrap the muscle-like material to make dynamic more like real-spine to deal with continuous model with chaotic system. 16 as shown in Figure 2.…”

Section: Reservoir Computingmentioning

confidence: 97%

“…14,15 The Reservoir Computing layer then feeds the transformed input into a linear readout layer to estimate the system's response, the benefit for Reservoir Computing is that it able to analysis chaotic system which suitable for soft robotics dynamic modeling. 13,16 The only trainable part of reservoir computing is the readout, which is ridge node, can be trained by state of training and y[t], where the state of training is the activation of the reservoir trigger by x[t], 10 as shown in Figure 1. Spine in the picture shown has four segments with three actuator, these actuator will generate force to balance the center of mass into the dynamic surface, 2, 8 this spine figure is the internal structure, it will wrap the muscle-like material to make dynamic more like real-spine to deal with continuous model with chaotic system.…”

Section: Reservoir Computingmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent design for quadruped robot on a dynamic flexible surface with an active spine

Liu

Lokhande

2023

Unmanned Systems Technology XXV

View full text Add to dashboard Cite

This paper proposes an approach to design a quadruped robot with a dynamic spine using Reservoir Computing. Four bending sensors are integrated into the continuous dynamics spine to adjust spine force for balancing and enable closed-loop control of the Center of Mass (CoMs) mapping onto the dynamic surface. Passively modeling the system enabled a quadrupedal robot with a flexible spine to navigate rough terrain with improved efficiency, agility, and minimized impact on explosive power. Additionally, paper introduce to develop a novel, intelligent control mechanism for the quadrupedal robot to operate effectively on dynamic, flexible surfaces. Our approach focuses on a hierarchical distributed control mechanism developed for leg to cooperatively change centralized frames for a better functional reflection.

show abstract

Section: Reservoir Computingmentioning

confidence: 97%

Section: Reservoir Computingmentioning

confidence: 99%

Intelligent design for quadruped robot on a dynamic flexible surface with an active spine

Liu

Lokhande

2023

Unmanned Systems Technology XXV

View full text Add to dashboard Cite

show abstract

“…Firstly, the inclusion of the anisotropic ratio and the myocardial layer for each ventricle in the optimization pipeline could give more degrees of freedom to match EAM data. Additionally, the parameter optimization schemes used by all participants of the CRT-Epiggy19 challenge were not taking advantage of recent technological advances such as the use of deep learning algorithms [55,56], variational approaches [57], reduced-order models [58,59] or GPUbased architectures [60], which allows for the exploration of a larger space of parameter solutions at reduced computational times. Moreover, cardiac multi-physical models should provide more realistic simulations, allowing for the inclusion of hemodynamic factors and improving the adjustment of CRT configuration through flow ratios [61], perfusion models [17], lumped models of the whole cardiovascular circulation [18] or with a complete torso [20].…”

Section: Limitations and Future Workmentioning

confidence: 99%

Meshless Electrophysiological Modeling of Cardiac Resynchronization Therapy—Benchmark Analysis with Finite-Element Methods in Experimental Data

et al. 2022

View full text Add to dashboard Cite

Computational models of cardiac electrophysiology are promising tools for reducing the rates of non-response patients suitable for cardiac resynchronization therapy (CRT) by optimizing electrode placement. The majority of computational models in the literature are mesh-based, primarily using the finite element method (FEM). The generation of patient-specific cardiac meshes has traditionally been a tedious task requiring manual intervention and hindering the modeling of a large number of cases. Meshless models can be a valid alternative due to their mesh quality independence. The organization of challenges such as the CRT-EPiggy19, providing unique experimental data as open access, enables benchmarking analysis of different cardiac computational modeling solutions with quantitative metrics. We present a benchmark analysis of a meshless-based method with finite-element methods for the prediction of cardiac electrical patterns in CRT, based on a subset of the CRT-EPiggy19 dataset. A data assimilation strategy was designed to personalize the most relevant parameters of the electrophysiological simulations and identify the optimal CRT lead configuration. The simulation results obtained with the meshless model were equivalent to FEM, with the most relevant aspect for accurate CRT predictions being the parameter personalization strategy (e.g., regional conduction velocity distribution, including the Purkinje system and CRT lead distribution).

show abstract

“…This makes the implementation of the reservoir computing relatively straightforward and therefore accessible for researcher form a wide range of backgrounds. Secondly, reservoir computing has been shown to perform well on predicting complex dynamics [3][4][5][6]. And, thirdly, due to the simplicity of the training method it is well suited for hardware implementation [7,8].…”

Section: Introductionmentioning

confidence: 99%

Reducing reservoir computer hyperparameter dependence by external timescale tailoring

Jaurigue,

Lüdge

2024

Neuromorph. Comput. Eng.

View full text Add to dashboard Cite

Task specific hyperparameter tuning in reservoir computing is an open issue, and is of particular relevance for hardware implemented reservoirs. We investigate the influence of directly including externally controllable task specific timescales on the performance and hyperparameter sensitivity of reservoir computing approaches. We show that the need for hyperparameter optimisation can be reduced if timescales of the reservoir are tailored to the specific task. Our results are mainly relevant for temporal tasks requiring memory of past inputs, for example chaotic timeseries prediciton. We consider various methods of including task specific timescales in the reservoir computing approach and demonstrate the universality of our message by looking at both time-multiplexed and spatially multiplexed reservoir computing.

show abstract

Prediction of chaotic time series using recurrent neural networks and reservoir computing techniques: A comparative study

Cited by 40 publications

References 39 publications

Intelligent design for quadruped robot on a dynamic flexible surface with an active spine

Intelligent design for quadruped robot on a dynamic flexible surface with an active spine

Meshless Electrophysiological Modeling of Cardiac Resynchronization Therapy—Benchmark Analysis with Finite-Element Methods in Experimental Data

Reducing reservoir computer hyperparameter dependence by external timescale tailoring

Contact Info

Product

Resources

About