Time Shifts to Reduce the Size of Reservoir Computers
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Abstract (arXiv)
A reservoir computer is a type of dynamical system arranged to do computation. Typically, a reservoir computer is constructed by connecting a large number of nonlinear nodes in a network that includes recurrent connections. In order to achieve accurate results, the reservoir usually contains hundreds to thousands of nodes. This high dimensionality makes it difficult to analyze the reservoir computer using tools from dynamical systems theory. Additionally, the need to create and connect large numbers of nonlinear nodes makes it difficult to design and build analog reservoir computers that can be faster and consume less power than digital reservoir computers. We demonstrate here that a reservoir computer may be divided into two parts; a small set of nonlinear nodes (the reservoir), and a separate set of time-shifted reservoir output signals. The time-shifted output signals serve to increase the rank and memory of the reservoir computer, and the set of nonlinear nodes may create an embedding of the input dynamical system. We use this time-shifting technique to obtain excellent performance from an opto-electronic delay-based reservoir computer with only a small number of virtual nodes. Because only a few nonlinear nodes are required, construction of a reservoir computer becomes much easier, and delay-based reservoir computers can operate at much higher speeds.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 7 of its 53 references that are in the map. First posted on arXiv 3 May 2022.
Builds on
7 of 53 references.
References · 7
- 2021Next Generation Reservoir Computingcited by 160
- 2020Do Reservoir Computers Work Best at the Edge of Chaos?cited by 17
- 2018Rapid Time Series Prediction with a Hardware-Based Reservoir Computercited by 14
- 2022Optimizing Memory in Reservoir Computerscited by 13
- 2019Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Linkscited by 10
- 2019Fundamental aspects of noise in analog-hardware neural networkscited by 5
- 2021Reservoir Computers with Random and Optimized Time-Shiftscited by 5
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Citing papers per year: 2022: 1; 2023: 2.
3 papers.
- 2023Attractor reconstruction with reservoir computers: The effect of the reservoir's conditional Lyapunov exponents on faithful attractor reconstructioncited by 9
- 2023Controlling dynamical systems to complex target states using machine learning: next-generation vs. classical reservoir computingcited by 3
- 2022Time-shift selection for reservoir computing using a rank-revealing QR algorithmcited by 1
- 2023Attractor reconstruction with reservoir computers: The effect of the reservoir's conditional Lyapunov exponents on faithful attractor reconstruction30 Dec 2023
- 2023Controlling dynamical systems to complex target states using machine learning: next-generation vs. classical reservoir computing14 Jul 2023
- 2022Time-shift selection for reservoir computing using a rank-revealing QR algorithm29 Nov 2022