Composite FORCE learning of chaotic echo state networks for time-series prediction
Echo state network (ESN), a kind of recurrent neural networks, consists of a fixed reservoir in which neurons are connected randomly and recursively and obtains the desired output only by training output connection weights. First-order reduced and controlled error (FORCE) learning is an online supervised training approach that can change the chaotic activity of ESNs into specified activity patterns. This paper proposes a composite FORCE learning method based on recursive least squares to train ESNs whose initial activity is spontaneously chaotic, where a composite learning technique featured by dynamic regressor extension and memory data exploitation is applied to enhance parameter convergence. The proposed method is applied to a benchmark problem about predicting chaotic time series generated by the Mackey-Glass system, and numerical results have shown that it significantly improves learning and prediction performances compared with existing methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Time SeriesTime Series AnalysisTime Series PredictionSimilar Papers 제목 키워드 기반
Robust Optimization and Validation of Echo State Networks for learning chaotic dynamics
An approach to the time-accurate prediction of chaotic solutions is by learning temporal patterns from data. Echo State Networks (ESNs), which are a class of Reservoir Computing, can accurately predict the chaotic dynami…
Bayesian OptimizationRobust DesignTime Series AnalysisGradient-free optimization of chaotic acoustics with reservoir computing
We develop a versatile optimization method, which finds the design parameters that minimize time-averaged acoustic cost functionals. The method is gradient-free, model-informed, and data-driven with reservoir computing b…
Using Echo-State Networks to Reproduce Rare Events in Chaotic Systems
We apply the Echo-State Networks to predict the time series and statistical properties of the competitive Lotka-Volterra model in the chaotic regime. In particular, we demonstrate that Echo-State Networks successfully le…
Time SeriesAutomatic-differentiated Physics-Informed Echo State Network (API-ESN)
We propose the Automatic-differentiated Physics-Informed Echo State Network (API-ESN). The network is constrained by the physical equations through the reservoir's exact time-derivative, which is computed by automatic di…
Denoising of discrete-time chaotic signals using echo state networks
Noise reduction is a relevant topic when considering the application of chaotic signals in practical problems, such as communication systems or modeling biomedical signals. In this paper an echo state network (ESN) is em…
Denoising