Chaos最新文献_第8页

Adaptive control of dynamical systems using reservoir computing. 基于储层计算的动态系统自适应控制。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0291585

Swarnendu Mandal, Swati Chauhan, Umesh Kumar Verma, Manish Dev Shrimali, Kazuyuki Aihara

引用次数: 0

Flatness-based control for generalized synchronization of chaotic systems with large dissipation and dimension mismatch. 基于平面度的大耗散和尺寸失配混沌系统广义同步控制。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0276030

Christophe Letellier, Ludovico Minati, Jean-Pierre Barbot, Irene Sendiña-Nadal, I Leyva

引用次数: 0

Controlling complex rhythms: A hierarchical approach to limit cycle switching. 控制复杂节奏：极限环切换的分层方法。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0296708

Suvam Pal, Dibakar Ghosh, Sandip Saha

引用次数: 0

Echo state and band-pass networks with aqueous memristors: Leaky reservoir computing with a leaky substrate. 带水忆阻器的回声状态和带通网络：带漏基板的漏储层计算。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0273574

T M Kamsma, J J Teijema, R van Roij, C Spitoni

{"title":"Echo state and band-pass networks with aqueous memristors: Leaky reservoir computing with a leaky substrate.","authors":"T M Kamsma, J J Teijema, R van Roij, C Spitoni","doi":"10.1063/5.0273574","DOIUrl":"https://doi.org/10.1063/5.0273574","url":null,"abstract":"Recurrent Neural Networks (RNNs) are extensively employed for processing sequential data such as time series. Reservoir computing (RC) has drawn attention as an RNN framework due to its fixed network that does not require training, making it an attractive platform for hardware-based machine learning. We establish an explicit correspondence between the well-established mathematical RC implementations of echo state networks and band-pass networks with leaky integrator nodes on the one hand and a physical circuit containing iontronic simple volatile memristors on the other. These aqueous iontronic devices employ ion transport through water as signal carriers and feature a voltage-dependent (memory) conductance. The activation function and the dynamics of the leaky integrator nodes naturally materialize as the (dynamic) conductance properties of iontronic memristors, while a simple fixed local current-to-voltage update rule at the memristor terminals facilitates the relevant matrix coupling between nodes. We process various time series, including pressure data from simulated airways during breathing that can be directly fed into the network due to the intrinsic responsiveness of iontronic devices to applied pressures. We accomplish this by employing established physical equations of motion of iontronic memristors for the internal dynamics of the circuit.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145039237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A mean-field approximation-based linearization framework for network reconstruction from binary time series. 基于平均场近似的二值时间序列网络重构线性化框架。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0279712

Ying-Yu Zhang, Hai-Feng Zhang, Xiao Ding, Chuang Ma

{"title":"A mean-field approximation-based linearization framework for network reconstruction from binary time series.","authors":"Ying-Yu Zhang, Hai-Feng Zhang, Xiao Ding, Chuang Ma","doi":"10.1063/5.0279712","DOIUrl":"https://doi.org/10.1063/5.0279712","url":null,"abstract":"A captivating challenge in network research is the reconstruction of complex network structures from limited binary-state time series data. Although some reconstruction approaches based on dynamical rules or sparse system of linear equations have been proposed, these approaches either rely on known dynamical rules, limiting their generality, or the system of linear equations is often empirically determined, with weak interpretability and the performance being sensitive to parameter settings. To address these limitations, we propose a network reconstruction method based on linearization grounded in mean-field approximation. By incorporating the mean-field approximation, the interpretability of the linearization process is enhanced. The method exploits a common feature of binary-state dynamics-nodes become active under the influence of active neighbors-and is independent of any specific dynamical model, thus ensuring broad applicability. While the structure of the linearization coefficients suggests a data partitioning (blocking) strategy, this approach is often computationally complex. To overcome this, we develop a non-blocking, parameter-free alternative and theoretically demonstrate that it achieves reconstruction performance comparable to that of the ideal blocking method. Finally, we conduct extensive tests on both artificial and real networks to verify the effectiveness of our approach and demonstrate its robustness using noisy binary state time series data.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145029050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The role of inhibitory neuronal variability in modulating phase diversity between coupled networks. 抑制性神经元变异性在耦合网络之间调制相位多样性中的作用。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0271348

Katiele V P Brito, Joana M G L Silva, Claudio R Mirasso, Fernanda S Matias

{"title":"The role of inhibitory neuronal variability in modulating phase diversity between coupled networks.","authors":"Katiele V P Brito, Joana M G L Silva, Claudio R Mirasso, Fernanda S Matias","doi":"10.1063/5.0271348","DOIUrl":"https://doi.org/10.1063/5.0271348","url":null,"abstract":"Neuronal heterogeneity, characterized by a multitude of spiking neuronal patterns, is a widespread phenomenon throughout the nervous system. In particular, the brain exhibits strong variability among inhibitory neurons. Despite the huge neuronal heterogeneity across brain regions, which in principle could decrease synchronization due to differences in intrinsic neuronal properties, cortical areas coherently oscillate during various cognitive tasks. Therefore, the functional significance of neuronal heterogeneity remains a subject of active investigation. Previous studies typically focus on the role of heterogeneity in the dynamic properties of only one population. Here, we explore how different types of inhibitory neurons can contribute to the diversity of the phase relations between two cortical areas. This research sheds light on the potential impact of local properties, such as neuronal variability, on communication between distant brain regions. We show that both homogeneous and heterogeneous inhibitory networks can exhibit phase diversity and nonintuitive regimes such as anticipated synchronization (AS) and phase bistability. It has been proposed that the bistable phase could be related to bistable perception, such as in the Necker cube, where the brain alternates between two interpretations of a static image. Moreover, we show that heterogeneity enlarges the region of zero-lag synchronization and bistability. We also demonstrate that the parameter controlling inhibitory heterogeneity modulates the transition from the usual delayed synchronization regime (DS) to AS. Finally, we show that inhibitory heterogeneity drives the internal dynamics of the free-running population. Therefore, we suggest a possible mechanism to explain when the DS-AS transition occurs via zero-lag synchronization or bistability.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145029078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Denoising and reconstruction of nonlinear dynamics using truncated reservoir computing. 截断油藏计算非线性动力学去噪与重建。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0273505

Omid Sedehi, Manish Yadav, Merten Stender, Sebastian Oberst

{"title":"Denoising and reconstruction of nonlinear dynamics using truncated reservoir computing.","authors":"Omid Sedehi, Manish Yadav, Merten Stender, Sebastian Oberst","doi":"10.1063/5.0273505","DOIUrl":"https://doi.org/10.1063/5.0273505","url":null,"abstract":"Measurements acquired from distributed physical systems are often sparse and noisy. Therefore, signal processing and system identification tools are required to mitigate noise effects and reconstruct unobserved dynamics from limited sensor data. However, this process is particularly challenging because the fundamental equations governing the dynamics are largely unavailable in practice. Reservoir Computing (RC) techniques have shown promise in efficiently simulating dynamical systems through an unstructured and efficient computation graph comprising a set of neurons with random connectivity. However, the potential of RC to operate in noisy regimes and distinguish noise from the primary smooth or non-smooth deterministic dynamics of the system has not been fully explored. This paper presents a novel RC method for noise filtering and reconstructing unobserved nonlinear dynamics, offering a novel learning protocol associated with hyperparameter optimization. The performance of the RC in terms of noise intensity, noise frequency content, and drastic shifts in dynamical parameters is studied in two illustrative examples involving the nonlinear dynamics of the Lorenz attractor and the adaptive exponential integrate-and-fire system. It is demonstrated that denoising performance improves by truncating redundant nodes and edges of the reservoir, as well as by properly optimizing hyperparameters, such as the leakage rate, spectral radius, input connectivity, and ridge regression parameter. Furthermore, the presented framework shows good generalization behavior when tested for reconstructing unseen and qualitatively different attractors. Compared to the extended Kalman filter, the presented RC framework yields competitive accuracy at low signal-to-noise ratios and high-frequency ranges.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144991657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Numerical methods for time-fractional subdiffusion equations: Convolution quadrature with block generalized Adams methods. 时间分数次扩散方程的数值方法：用块广义Adams方法卷积求积分。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0264522

Ling Liu, Jinrong Wang

引用次数: 0

Application of nonlinear stochastic fractional systems in the generalized SEIR model. 非线性随机分数系统在广义SEIR模型中的应用。

IF 3.2 2区数学

Chaos Pub Date : 2025-08-01 DOI: 10.1063/5.0279918

H Tamimi, R Saadati, M B Ghaemi

引用次数: 0

Using unstable periodic orbits to understand blocking behavior in a low order land-atmosphere model. 利用不稳定周期轨道来理解低阶陆地-大气模型中的阻塞行为。