Time Parameter Optimization for the Semiconductor Laser-Based Time-Delay Reservoir Computing System

Time-delay reservoir computing (RC) systems, particularly those based on semiconductor lasers (SLs), have gained attention due to their low energy consumption, high response rates, and rich nonlinear dynamics. This work investigates the influence of key time parameters—virtual node interval (θ), delay feedback (τ), and data injection period (T) on the performance of SL-based time-delay RC systems. Using the Santa Fe time series prediction task and memory capacity evaluation task, we analyze how these parameters affect prediction accuracy and memory capability. The results reveal that θ = 0.2Tro (where Tro is the relaxation oscillation period of the SLs) optimizes prediction performance, while θ = 0.5Tro maximizes memory capacity. Additionally, feedback delay τ significantly impacts system performance. Shorter τ values (e.g., τ = 0.54T) enhance prediction accuracy, whereas longer τ values (e.g., τ = 1.74T) improve memory capacity. These findings provide valuable insights for optimizing time-delay RC systems, enabling better task-specific performance and stability.