[1] Feifan YANG, Xiaoguang LI, Li ZHUO. Image deblurring of dynamic scene based on attention residual CODEC network. Journal of Applied Optics, 42, 685-690(2021).
[2] Mengxiao YIN, Zhenfeng Lin, Feng YANG. Adaptive multi-scale information fusion based on dynamic receptive field for image-to-image translation. Journal of Electronics & Information Technology, 43, 2386-2394(2021).
[3] Y SHEN, N C HARRIS, S SKIRLO et al. Deep learning with coherent nanophotonic circuits. Nature Photonics, 11, 441-446(2017).
[4] X LUO, Y HU, X OU et al. Metasurface-enabled on-chip multiplexed diffractive neural networks in the visible. Light: Science & Applications, 11, 158(2022).
[5] T ZHOU, X LIN, J WU et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nature Photonics, 15, 367-373(2021).
[6] M NAKAJIMA, K TANAKA, T HASHIMOTO. Scalable reservoir computing on coherent linear photonic processor. Communications Physics, 4, 20(2021).
[7] K VANDOORNE, P MECHET, Vaerenbergh T VAN et al. Experimental demonstration of reservoir computing on a silicon photonics chip. Nature Communications, 5, 3541(2014).
[8] DING B, PEI L, BAI B, et al. The computing chips in unmanned systems from electron to photon[C]International Conference on Autonomous Unmanned Systems, Singape: Springer Nature Singape, 2022: 36433652.
[9] X ZHANG, H F QIU, J Q LAN. Programmable gate array WM-TDLAS gas detection system design and application. Opto-Electron Eng, 51, 240022(2024).
[10] A LUGNAN, A KATUMBA, F LAPORTE et al. Photonic neuromorphic information processing and reservoir computing. APL Photonics, 5, 020901(2020).
[11] I BAUWENS, K HARKHOE, P BIENSTMAN et al. Transfer learning for photonic delay-based reservoir computing to compensate parameter drift. Nanophotonics, 12, 949-961(2023).
[12] I BAUWENS, der SANDE G Van, P BIENSTMAN et al. Using photonic reservoirs as preprocessors for deep neural networks. Frontiers in Physics, 10, 1051941(2022).
[13] XIA G Q, HOU Y S, WU Z M. Prediction perfmance of reservoir computing using a semiconduct laser with double optical feedback[C]2018 Conference on Lasers ElectroOptics Pacific Rim (CLEOPR), IEEE, 2018: 12.
[14] J NAKAYAMA, K KANNO, A UCHIDA. Laser dynamical reservoir computing with consistency: An approach of a chaos mask signal. Optics Express, 24, 8679-8692(2016).
[15] S SUNADA, A UCHIDA. Photonic neural field on a silicon chip: large-scale, high-speed neuro-inspired computing and sensing. Optica, 8, 1388-1396(2021).
[16] Van der SE G, HARKHOE K, KATUMBA A, et al. Integrated photonic delaylasers f reservoir computing[C]Physics Simulation of Optoelectronic Devices XXVIII. SPIE, 2020, 11274: 4147.
[17] K TAKANO, C SUGANO, M INUBUSHI et al. Compact reservoir computing with a photonic integrated circuit. Optics Express, 26, 29424-29439(2018).
[18] D BRUNNER, I FISCHER. Reconfigurable semiconductor laser networks based on diffractive coupling. Optics Letters, 40, 3854-3857(2015).
[19] J DONG, M RAFAYELYAN, F KRZAKALA et al. Optical reservoir computing using multiple light scattering for chaotic systems prediction. IEEE Journal of Selected Topics in Quantum Electronics, 26, 1-12(2019).
[20] SACKESYN S, MA C, KATUMBA A, et al. A powerefficient architecture f onchip reservoir computing[C]Artificial Neural wks Machine Learning–ICANN 2019, 2019: 161164.
[21] A KATUMBA, M FREIBERGER, F LAPORTE et al. Neuromorphic computing based on silicon photonics and reservoir computing. IEEE Journal of Selected Topics in Quantum Electronics, 24, 2821843(2018).
[22] C MESARITAKIS, V PAPATAXIARHIS, D SYVRIDIS. Microring resonators as building blocks for an all-optical high-speed reservoir-computing bit-pattern-recognition system. JOSA B, 30, 3048-3055(2013).
[23] A KATUMBA, X YIN, J DAMBRE et al. A neuromorphic silicon photonics nonlinear equalizer for optical communications with intensity modulation and direct detection. Journal of Lightwave Technology, 37, 2232-2239(2019).
[24] S SACKESYN, C MA, J DAMBRE et al. Experimental realization of integrated photonic reservoir computing for nonlinear fiber distortion compensation. Optics Express, 29, 30991-30997(2021).
[25] DENISLE COARER F, RONTANI D, KATUMBA A, et al. Toward neuroinspired computing using a small wk of microring resonats on an integrated photonic chip[C]Neuroinspired Photonic Computing, SPIE, 2018, 10689: 1422.
[26] COARER F DENIS-LE, M SCIAMANNA, A KATUMBA et al. All-optical reservoir computing on a photonic chip using silicon-based ring resonators. IEEE Journal of Selected Topics in Quantum Electronics, 24, 2836985(2018).
[27] F LAPORTE, A KATUMBA, J DAMBRE et al. Numerical demonstration of neuromorphic computing with photonic crystal cavities. Optics Express, 26, 7955-7964(2018).
[28] A KATUMBA, M FREIBERGER, P BIENSTMAN et al. A multiple-input strategy to efficient integrated photonic reservoir computing. Cognitive Computation, 9, 307-314(2017).
[29] H JAEGER. The “echo state” approach to analysing and training recurrent neural networks-with an erratum note. Bonn, Germany: German National Research Center for Information Technology GMD Technical Report, 148, 13(2001).
[30] W MAASS, T NATSCHLäGER, H MARKRAM. Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation, 14, 2531-2560(2002).
[31] D VERSTRAETEN, B SCHRAUWEN, M D’HAENE et al. An experimental unification of reservoir computing methods. Neural Networks, 20, 391-403(2007).
[32] B LIU, Y XIE, X JIANG et al. Forecasting stock market with nanophotonic reservoir computing system based on silicon optomechanical oscillators. Optics Express, 30, 23359-23381(2022).
[33] W J WANG, Y TANG, J XIONG et al. Stock market index prediction based on reservoir computing models. Expert Systems with Applications, 178, 115022(2021).
