A Multiple-Input Multiple-Output Reservoir Computing System Subject to Optoelectronic Feedbacks and Mutual Coupling

Abstract

In this paper, a multiple-input multiple-output reservoir computing (RC) system is proposed, which is composed of multiple nonlinear nodes (Mach–Zehnder modulators) and multiple mutual-coupling loops of optoelectronic delay lines. Each input signal is added into every mutual-coupling loop to implement the simultaneous recognition of multiple route signals, which results in the signal processing speed improving and the number of routes increasing. As an example, the four-route input and four-route output RC is simultaneously realized by numerical simulations. The results show that this type of RC system can successfully recognize the four-route optical packet headers with 3-bit, 8-bit, 16-bit, and 32-bit, and four-route independent digital speeches. When the white noise is added to the signals such that the signal-to-noise ratio (SNR) of the optical packet headers and the digital speeches are 35 dB and 20 dB respectively, the normalized root mean square errors (NRMSEs) of the signal recognition are all close to 0.1. The word error rates (WERs) of the optical packet header recognition are 0%. The WER of the digital speech recognition is 1.6%. The eight-route input and eight-route output RC is also numerically simulated. The recognition of the eight-route 3-bit optical packet headers is implemented. The parallel processing of multiple-route signals and the high recognition accuracy are implemented by this proposed system.

Performance Enhancement of a Delay-Based Reservoir Computing System by Using Gradient Boosting Technology

“…τ is the feedback time. In this work, the synchronization scheme is adopted, i. e. τ = T [31], [32].…”

Entropy-Based Algorithms for Signal Processing

“…A modified cross-entropy loss function is proposed to train their deep model. A PointRend algorithm is used to recover a smooth, clear, and sharp road boundary [ 3 ]. The augmented DeepGlobe dataset is used to train their deep model, and the asynchronous training method is applied to accelerate the training process.…”

“…In the contribution by Bao et al [ 3 ], “A Multiple-Input Multiple-Output Reservoir Computing System Subject to Optoelectronic Feedbacks and Mutual Coupling,” a multiple-input multiple-output reservoir computing (RC) system is proposed. The system is composed of multiple nonlinear nodes (Mach–Zehnder modulators) and multiple mutual-coupling loops of optoelectronic delay lines.…”

Numerical Demonstration of the Transmission of Low Frequency Fluctuation Dynamics Generated by a Semiconductor Laser with Optical Feedback

“…Semiconductor lasers with optical feedback can generate various nonlinear physical phenomena and can be applied in secure communication, random number generation, lidar, comprehensive sensing, and reservoir computing, etc. [1][2][3][4][5][6][7]. Moreover, the low frequency fluctuation (LFF) dynamic induced by the optical feedback architecture has been found to be a type of excitable behavior [8], which can be exploited for photonic neurons [9][10][11][12][13] and neuromorphic computations such as pattern recognition, logic operations, and calculations [14][15][16][17][18][19].…”