Enhanced performance of a reservoir computing system based on a dual-loop optoelectronic oscillator

Abstract

Time-delayed reservoir computing (RC) is a brain inspired paradigm for processing temporal information, with simplification in the network’s architecture using virtual nodes embedded in a temporal delay line. In this work, a novel, to the best of our knowledge, RC system based on a dual-loop optoelectronic oscillator is proposed to enhance the prediction and classification. The hardware is compact and easy to implement, and only a section of fiber compared to the traditional optoelectronic oscillator reservoir is added to conform the dual-loop scheme. Compared with the traditional reservoir, a remarkable performance of the proposed RC system is demonstrated by simulation on three well-known tasks, namely the nonlinear auto regressive moving average (NARMA10) task, signal waveform recognized task, and handwritten numeral recognition. The parameter optimization in the NARMA10 task is presented with influenced factors. The novel RC system finally obtains a normalized mean square error at 0.0493 ± 0.007 in NARMA10 task, 6.172 × 1 0 − 6 in signal waveform recognized task, and a word error rate at 9% in handwritten numeral recognition with suitable parameters.