In this work, we propose and numerically investigate a scheme for reservoir computing (RC) based on two parallel reservoirs under identical electrical message injection, in which two semiconductor lasers (SLs) under optical feedback are utilized as two parallel reservoirs. For simplifying the system, only one mask signal is employed in this scheme. After multiplying with input data, the masked information is injected into two parallel reservoir lasers (SL ₁ and SL ₂ ). The virtual node states can be obtained from the temporal outputs of two SLs. RC can be accomplished by three ways, namely RC ₁ /RC ₂ (the virtual node states originating from SL ₁ /SL ₂ are used for training and testing) and RC _M (the merged virtual node states originating from two SLs are used for training and testing). Via chaotic time series prediction task and memory capacity (MC) test, the performance of the RC system is simulated and assessed. The results show that, for a given data processing rate, better prediction performance and higher MC can be realized by RC _M through setting suitable mismatched parameters between the two SLs. Under satisfying the requirement for achieving a good performance, the highest data processing rate can be doubled for RC _M with respect to that for RC ₁ /RC ₂ .

We experimentally investigate the performances of an optical reservoir computing (RC) system based on two parallel time-delay reservoirs composed of two semiconductor lasers (SLs) subject to optical feedback. In such a system, the information being processed is split into two parts to send into two reservoirs through directly modulating the pump currents of two SLs, and the temporal output of the two SLs are sampled and taken as the virtual node states for training and testing. Via Santa Fe time series prediction task and multi-waveform recognition task, the performances of the proposed RC system are investigated and compared with those of the system based on one reservoir. The results show that the system based on two parallel reservoirs behaves better performance and stronger parameter robustness than that based on one reservoir. Moreover, through analyzing the dependence of the system performances on the number of virtual node states actually used for readout, the potential data processing rate (DPR) of the system is evaluated. For processing a prediction task under guaranteeing the normalized mean square error below 0.1 and a recognition task under guaranteeing the signal error rate below 0.005, the potential DPR of the proposed RC system can achieve 200 MSa/s, which is twice the DPR of the system with only one reservoir.

In this work, the performance of an optoelectronic time-delay reservoir computing system for performing a handwritten digit recognition task is numerically investigated, and a scheme to improve the recognition speed using multiple parallel reservoirs is proposed. By comparing four image injection methods based on a single time-delay reservoir, we find that when injecting the histograms of oriented gradient (HOG) features of the digit image, the accuracy rate (AR) is relatively high and is less affected by the offset phase. To improve the recognition speed, we construct a parallel time-delay reservoir system including multi-reservoirs, where each reservoir processes part of the HOG features of one image. Based on 6 parallel reservoirs with each reservoir possessing 100 virtual nodes, the AR can reach about 97.8%, and the reservoir processing speed can reach about 1 × 10⁶ digits per second. Meanwhile, the parallel reservoir system shows strong robustness to the parameter mismatch between multi-reservoirs.

We propose and demonstrate experimentally a scheme for simultaneously generating multi-channel broadband chaotic signals based on two unidirectionally coupled weak-resonant-cavity Fabry-Perot laser diodes (WRC-FPLDs) with almost the same mode interval, where one WRC-FPLD is utilized as master WRC-FPLD (M-WRC-FPLD) and the other is used as slave WRC-FPLD (S-WRC-FPLD). Under the optical injection from the M-WRC-FPLD with suitable injection parameters, multiple longitudinal modes of the S-WRC-FPLD can be simultaneously driven into chaotic states, which can provide multi-channel chaotic signals. Via a tunable optical filter, we inspect the performances of 9-channel chaotic signals, the results show that the chaotic bandwidths are within the range of 8~15 GHz for all the 9-channel chaotic signals. Furthermore, the influences of the injection power and frequency detuning on the performances of the chaotic output from one of the channels are also analyzed.

We numerically investigate the performance of Chinese herbal medicine (CHM) recognition using a time delay reservoir computing (TDRC) system consist of a vertical-cavity surface-emitting laser (VCSEL) subjected to optical feedback. A database of 960 images from 12 CHM categories is used to evaluate the recognition performance, most of which contain many CHM samples and have different backgrounds. The CHM image feature vector is masked and optically injected into the VCSEL, and the VCSEL output in two polarization modes are sampled as the virtual node states for readout. After testing the CHM recognition performance of TDRC under injecting the same information into two polarization modes of the VCSEL, we further test the performance of parallel processing by injecting half of the image information into each of the two polarization modes. By concatenating the virtual node states collected from two modes for readout, the recognition speed is doubled. Meanwhile, the influence of key parameters on recognition performance are investigated and the relationship between system idle state and recognition performance is also examined. The results show that, under parallel processing and orthogonal optical feedback, the TDRC achieves the minimum recognition error of 1.7% at a reservoir processing rate of 2×10 ⁹ images per second.

The performance of an optoelectronic reservoir computing (RC) system for recognizing apple leaf diseases is numerically investigated. The disease spot feature extraction and selection methods suitable for time-delay RC are investigated. Two masking methods of sequence extension masking and matrix transformation masking are examined, with appropriate parameters, the optoelectronic RC achieves minimum errors of 0.01 and 0.03 under sequence extension masking and matrix masking methods, respectively. The nonlinear dynamics of the optoelectronic reservoir in the absence of data injection are compared with its recognition performance to establish the relationship between recognition capabilities and the system’s nonlinear dynamics. Additionally, we compared the recognition errors of optoelectronic RC, linear RC, and k-nearest neighbor algorithms. It illustrates that optoelectronic RC, under the nonlinear transformation of the Mach-Zehnder Modulator (MZM) and delayed feedback, can map input information nonlinearly to a high-dimensional state space, enabling effective classification along linear hyperplanes in this high-dimensional space.