Deyu Cai , Penghua Mu , Yu Huang , Pei Zhou , Nianqiang Li
{"title":"由外部非对称双路滤波腔激光器辅助的加固型水库计算机","authors":"Deyu Cai , Penghua Mu , Yu Huang , Pei Zhou , Nianqiang Li","doi":"10.1016/j.chaos.2024.115652","DOIUrl":null,"url":null,"abstract":"<div><div>Chaos, characterized by irregular, stochastic-like and occurring in deterministic systems, is widely used in meteorology, life sciences, and physics. Precise chaos predictions are crucial for early warning of extreme weather and disease prevention. We propose a photonic time-delay reservoir computing (TDRC) system with asymmetric dual-path filtering optical feedback under optical injection for short-term prediction of chaotic time series. To thoroughly evaluate the performance in short-term prediction provided by such TDRC, we assess two different chaotic time series, i.e., the Santa-Fe and Mackey-Glass chaotic time series, as well as the memory capacity. Numerical results indicate that the proposed TDRC outperforms the system with conventional dual-path optical feedback in short-term prediction performance. This is attributed to the enhanced memory capacity originating from the asymmetric dual-path filtering optical feedback. Additionally, we reveal the effects of the injection strength, feedback strength, filter bandwidth and the number of virtual nodes on the system performance. Our work provides a novel path for accurate short-term prediction of complex chaotic systems using photonic TDRC.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A reinforced reservoir computer aided by an external asymmetric dual-path-filtering cavity laser\",\"authors\":\"Deyu Cai , Penghua Mu , Yu Huang , Pei Zhou , Nianqiang Li\",\"doi\":\"10.1016/j.chaos.2024.115652\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Chaos, characterized by irregular, stochastic-like and occurring in deterministic systems, is widely used in meteorology, life sciences, and physics. Precise chaos predictions are crucial for early warning of extreme weather and disease prevention. We propose a photonic time-delay reservoir computing (TDRC) system with asymmetric dual-path filtering optical feedback under optical injection for short-term prediction of chaotic time series. To thoroughly evaluate the performance in short-term prediction provided by such TDRC, we assess two different chaotic time series, i.e., the Santa-Fe and Mackey-Glass chaotic time series, as well as the memory capacity. Numerical results indicate that the proposed TDRC outperforms the system with conventional dual-path optical feedback in short-term prediction performance. This is attributed to the enhanced memory capacity originating from the asymmetric dual-path filtering optical feedback. Additionally, we reveal the effects of the injection strength, feedback strength, filter bandwidth and the number of virtual nodes on the system performance. Our work provides a novel path for accurate short-term prediction of complex chaotic systems using photonic TDRC.</div></div>\",\"PeriodicalId\":9764,\"journal\":{\"name\":\"Chaos Solitons & Fractals\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-10-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chaos Solitons & Fractals\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0960077924012049\",\"RegionNum\":1,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chaos Solitons & Fractals","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0960077924012049","RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
A reinforced reservoir computer aided by an external asymmetric dual-path-filtering cavity laser
Chaos, characterized by irregular, stochastic-like and occurring in deterministic systems, is widely used in meteorology, life sciences, and physics. Precise chaos predictions are crucial for early warning of extreme weather and disease prevention. We propose a photonic time-delay reservoir computing (TDRC) system with asymmetric dual-path filtering optical feedback under optical injection for short-term prediction of chaotic time series. To thoroughly evaluate the performance in short-term prediction provided by such TDRC, we assess two different chaotic time series, i.e., the Santa-Fe and Mackey-Glass chaotic time series, as well as the memory capacity. Numerical results indicate that the proposed TDRC outperforms the system with conventional dual-path optical feedback in short-term prediction performance. This is attributed to the enhanced memory capacity originating from the asymmetric dual-path filtering optical feedback. Additionally, we reveal the effects of the injection strength, feedback strength, filter bandwidth and the number of virtual nodes on the system performance. Our work provides a novel path for accurate short-term prediction of complex chaotic systems using photonic TDRC.
期刊介绍:
Chaos, Solitons & Fractals strives to establish itself as a premier journal in the interdisciplinary realm of Nonlinear Science, Non-equilibrium, and Complex Phenomena. It welcomes submissions covering a broad spectrum of topics within this field, including dynamics, non-equilibrium processes in physics, chemistry, and geophysics, complex matter and networks, mathematical models, computational biology, applications to quantum and mesoscopic phenomena, fluctuations and random processes, self-organization, and social phenomena.