Abstract—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.

Index Terms—Nanophotonic reservoir computing, time series classification, photonic crystal cavities.

M. R. Salehi is with the Department of Electrical and Electronics Engineering, Shiraz University of Technology (Sutech), Modarres Boulevard, P.O, Box 16315-1618, Shiraz, Iran (e-mail: salehi@sutech.ac.ir; tel: +98 711 7353500; fax: +98 711 7354514).
E. Abiri and L. Dehyadegari are with the Department of Electronics, Shiraz University of Technology, P.O, Box 16315-1618, Shiraz, Iran (e-mail: abiri@sutech.ac.ir, l.dehyadegari@sutech.ac.ir).

 

Cite:M. R. Salehi, E. Abiri, and L. Dehyadegari, "Nanophotonic Reservoir Computing for Noisy Time Series Classification," International Journal of Computer and Electrical Engineering vol. 6, no.3, pp. 240-243, 2014.