Nanophotonic Reservoir Computing for Noisy Time Series Classification

Reservoir computing is known as a recent training concept in machine learning. This method is particularly useful in solving a broad category of categorization and recognition problems. The aim of this paper is using photonic reservoir computing for noisy time series classification. A complex network of photonic crystal cavities is used for modeling photonic reservoir computing. Applying nanophotonic reservoir computing resulted in perfect (100%) recognition accuracy for noise-less time series classification, and an accuracy of about 98% for noisy time series, which shows improvement (an amount of 3%) compared to previous works.