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Embedding theory of reservoir computing and reducing reservoir network using time delays

Xing-Yue Duan¹, Xiong Ying², Si-Yang Leng ^3,4,5, Jürgen Kurths ^3,6, Wei Lin ^2,3,5,7,*, and Huan-Fei Ma ^1,†

¹School of Mathematical Sciences, Soochow University, Suzhou 215006, China
²School of Mathematical Sciences, SCMS, and SCAM, Fudan University, Shanghai 200433, China
³Research Institute of Intelligent Complex Systems and Centre for Computational Systems Biology, Fudan University, Shanghai 200433, China
⁴Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
⁵State Key Laboratory of Medical Neurobiology, LCNBI, and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai 200032, China
⁶Potsdam Institute for Climate Impact Research (PIK), Potsdam 14473, Germany
⁷Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China

^*wlin@fudan.edu.cn
^†hfma@suda.edu.cn

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Articles Citing

Embedding theory of reservoir computing and reducing reservoir network using time delays

Machine Learning-Based Prediction Framework for Complex Neuromorphic Dynamics of Third-Order Memristive Neurons at the Edge of Chaos

Post-processing methods for delay embedding and feature scaling of reservoir computers

Reservoir computing with the minimum description length principle

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

Hybrid optimization for efficient pruning of randomly generated neural networks

Statistics for differential topological properties between datasets with an application to reservoir computers

New analytic formulas for memory and prediction functions in reservoir computers with time delays

Reservoir cross mapping as a nonlinear framework for detecting dynamical causality

No title available

Learning Hamiltonian neural Koopman operator and simultaneously sustaining and discovering conservation laws

Application of next-generation reservoir computing for predicting chaotic systems from partial observations

Polynomial function error stair of reservoir computing and its applications in characterizing the learning capability

Reducing reservoir computer hyperparameter dependence by external timescale tailoring

Emerging opportunities and challenges for the future of reservoir computing

Higher-order Granger reservoir computing: simultaneously achieving scalable complex structures inference and accurate dynamics prediction

Connectome-based reservoir computing with the conn2res toolbox

Memristor-based input delay reservoir computing system for temporal signal prediction

An organized view of reservoir computing: a perspective on theory and technology development

Time-delayed reservoir computing based on spin-VCSEL: interplay between pump ellipticity and performance

Learning from the past: reservoir computing using delayed variables

Detecting dynamical causality via intervened reservoir computing

Controlling chaotic maps using next-generation reservoir computing

Attractor reconstruction with reservoir computers: The effect of the reservoir’s conditional Lyapunov exponents on faithful attractor reconstruction

Model adaptive phase space reconstruction

Based on hypernetworks and multifractals: Deep distribution feature fusion for multidimensional nonstationary time series prediction

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