The European Physical Journal Special Topics最新文献

The main focus of this manuscript is vested in the introduction of a new Lyapunov–Krasovskii functional (LKF) for hybrid fractional-order neural networks (FONNs) with Lipschitz non-linearity. The primary originality of this work lies in exploring the possibility of using a new type of functionals similar to looped functional for the stability analysis of hybrid fractional-order systems (FOSs) with delays. Although some work in this direction has been attempted, the formal theory for this concept has not been developed yet. First, a new lemma on establishing asymptotic stability using an arbitrary looped-like LKF and the fractional-order Lyapunov direct method is derived. Using this result, new delay and sampling period dependent stability criteria for the considered FONNs are established in the form of LMIs. Lastly, numerical simulations validate the correctness of the theoretical results proposed in this manuscript.

This paper develops a data-driven deterministic identification architecture for discovering stochastic differential equations (SDEs) directly from data. The architecture first generates deterministic data for stochastic processes using the Feynman–Kac formula, and gives a parabolic partial differential equation (PDE) associated with the SDE. Then, a sparse regression model is proposed to discover drift and diffusion terms in SDEs using PDE data-driven techniques, where a large candidate library of potential terms only for the drift and diffusion coefficients in SDEs need be constructed. To simultaneously infer the drift and diffusion terms, we proposed a sequential thresholded reweighted least-squares algorithm to solve the constructed sparse regression model. The main advantage of the proposed method is that on the one hand, theoretical and numerical identification results of PDEs can be used for SDEs, on the score, our SDE identification problem is translated into the parameter estimation problem of PDEs, on the other hand, the proposed algorithm is easily executed and can enhance the sparsity and accuracy. Through several classical SDEs and ordinary differential equations, the effectiveness of the proposed data-driven method is demonstrated, and several comparison experiments with state-of-the-art approaches is provided to illustrate the superiority of the developed algorithm.

This study investigates the intricate role of myeloid-derived suppressor cells (MDSCs) in inhibiting the immunological responses against malignancies by employing a delayed fractional-order mathematical model. The proposed mathematical model includes tumor cells, dendritic cells, macrophages, cytotoxic T lymphocytes (CTLs), and MDSCs as components of a five-dimensional deterministic system. Further, the model accounts for the duration of the mechanism by which MDSCs perform immunosuppressive activities. One such mechanism involves the release of immunosuppressive cytokines such as interleukin-10 (IL-10), and it is elucidated by incorporation of the time-delay parameter, (tau). Basic properties of the system such as non-negativity and uniqueness of solutions as well as the existence of biologically feasible steady states are explored. The conditions for steady-state stability and existence of Hopf bifurcation concerning the delay parameter ((tau )) are established. We notice that the growth rate of cancer cells determines the stability nature of the tumor-free equilibrium, regardless of the time-delay (tau). While the fractional-order (alpha) does not affect the stability of steady states, however it does influence the transient behavior of the considered system.

These notes provide an introduction to phase ordering in dry, scalar active matter. We first briefly review Model A and Model B, the long-standing continuum descriptions of ordering in systems with a non-conserved and conserved scalar order parameter. We then contrast different ways in which the field theories can be extended so that the phase ordering persists, but in systems that are active and do not reach thermodynamic equilibrium. The active models allow a wide range of dynamical steady states not seen in their passive counterparts. These include microphase separation, active foams and travelling density bands.

This study concerns the exponential stability and stabilization problem for the Takagi–Sugeno (T–S) fuzzy systems under the membership function dependent (MFD) H(_infty )-based sampled-data control (SDC) using an improved exponential two-sided looped-functional (TSLF) approach. First, an exponential TSLF is introduced to achieve the state information within the intervals (t_{textrm{k}}) to t and t to (t_{textrm{k}+1}), which not only relaxes the monotonic constraint but would also not impose the terms as positive definite. By employing the concept of looped-functional, a new exponential TSLF is introducing the exponents (e^{2alpha (t-t_{textrm{k}})},) (frac{e^{2alpha (t_{textrm{k}+1}-t)}-1}{2alpha }), and (frac{e^{-2alpha (t-t_{textrm{k}})}-1}{2alpha }), thereby enhancing the control performance, design flexibility and also it contains the more actual sampling information of system states. Next, the new exponentially stable lemma is derived from the fuzzy SDC system with an external disturbance. To this end, the time derivatives of exponential TSLF-based Lyapunov–Krasovskii functional, some stabilization criteria exist in formulating linear matrix inequalities to secure the system is exponentially stable under MFD H(_infty ) criterion. Finally, the chaotic permanent magnet synchronous generator-based wind energy system is demonstrated under the practical values with proposed sufficient conditions, and Rossler’s model expresses the advantages and excellence of the proposed techniques.

By using an active disturbance reduction strategy, the current study addresses the tracking problem of descriptor semi-Markovian jump systems against unknown nonlinear uncertainties (UNC) and aperiodic disturbances. In contrast to previous studies, the improved equivalent-input-disturbance (IEID) estimator is merged with the generalized modified repetitive control (GMRC) rule. To do this, initially, the state-space model of GMRC is proposed. Then, the composite IEID estimator-based GMRC is designed to regulate UNC and disturbances and ensures desired output tracking performance of the proposed model. By constructing augmented Lyapunov–Krasovskii functional, sufficient conditions are presented to guarantee mean-square asymptotic admissibility of closed-loop dynamics in terms of linear matrix inequalities. Finally, the dynamics of DC motor structure with two modes are validated with derived sufficient conditions. The simulation and comparison results confirmed the better tracking performances over the existing methods.

The experimental methodologies of (gamma)-ray detection and spectroscopy are widely used in nuclear astrophysics research that typically centers on the measurement of cross sections of reactions constituting the network of stellar nucleosynthesis. This article identifies the key factors of such endeavors and analyzes their impact on the aspired objectives. Such perspectives are known to be taken into cognizance while planning a facility for nuclear astrophysics research as well as in defining a research programme therein.

The paper is concerned with the development of a sampled-data (SD) controller designed for Takagi–Sugeno (T–S) fuzzy systems. A key highlight is the incorporation of a refined fractional delayed-state into this control approach. The primary aim is to establish criteria for system stabilization, thereby ensuring the asymptotic stability of the considered systems. This objective is pursued within the framework of the newly designed control methodology. The core contribution of work explains in the introduction of an innovative Lyapunov–Krasovskii functional (LKF) tailored to T–S fuzzy systems. This novel approach capitalizes on the efficacy of sampling intervals. The LKF design takes advantage of variable attributes tied to the real sampling pattern, effectively reducing the conservatism of the outcomes. Moreover, the paper introduces a sophisticated fractional delayed-state concept, which plays a pivotal role in shaping a modified looped functional-based LKF. The stability criteria are then formulated through the utilization of linear matrix inequalities (LMIs) and integral inequalities. These criteria perform dynamic part in establishing the asymptotic stability of considered systems when subjected to the designed control approach.

Time-varying and seasonal parameter inversion in the mathematical model of infectious diseases and uncertainty quantization based on actual data have great significance for real quantitative transmission process. In this study, the behavior-driven mathematical model of infectious diseases and the data-driven parameter identification method are combined to quantify the transmission law of tuberculosis (TB). To begin with, according to the characteristics of TB transmission, the TS-SID model with time-varying is established. Then, the improved identification algorithm is proposed to track the fluctuation of disease infection rate and mortality rate considering the seasonal influence. Meanwhile, focusing on the influence of noise on the spread of diseases, noise reduction and uncertain quantization are carried out on the data to identify the noise distribution. In addition, predict the denoised sequence and superimpose the noise distribution, which can improve the rationality of prediction. Finally, the numerical comparison shows that seasonal time-varying tracking is good for grasping and predicting the disease evolution.

The cerebrum and cerebellum play a crucial role in motion control and are crucial to perform a variety of fast, precise movements for humans and animals. Emotions are generated in the cerebral cortex, and activate the amygdala, which promotes the storage of information in various regions of the cerebrum. In this paper, cerebellar learning model, emotional learning model, and spinal cord calculation module are incorporated to complete the control of an arm musculoskeletal system, and the redundancy problem of the musculoskeletal system control is solved through the optimized calculation in the spinal cord module. The arm musculoskeletal system can thus complete the end trajectory execution task successfully. It is shown that compared with the cerebellar motion control scheme, the proposed scheme has the advantages of fast learning convergence, simplified synaptic adaptation of cerebellum and strong anti-disturbance ability. It is also verified that the proposed control scheme exhibits good robustness to random noise. The proposed arm musculoskeletal control scheme operates effectively and provides a theoretical reference for the application of biomimetic musculoskeletal system.