Enhancing Prediction Performance of Reservoir Computing by Multiple Delayed Feedbacks

Time delay reservoir computers are machine learning tools that use the infinite-dimensional property of delay-differential equations to do tasks such as chaotic time-series predictions. They have the advantage of being easy to implement numerically and experimentally. It is essential to carefully select the time delay and other parameters to achieve a good prediction with the slightest error. For example, it has been found that whenever the time delay and clock cycle are identical, the target's prediction worsens. Previously we have shown that varying the spacing between delays can suppress chaotic dynamics in the first-order nonlinear time delay systems. As a result of adding delays with small spacing to the Electro-optic oscillator model with filter, a stronger feedback coefficient is needed to destabilize the system's dynamics. Our study examined the impact of the time delays and the spacing between them on the reservoir computing device's performance. According to the input’s complexity and correlation, different time delay configurations may be used for different tasks.