System Of Deterministic Equations Research Articles

Response analysis of asymmetric monostable energy harvester with an uncertain parameter

The nonlinear piezoelectric vibration energy harvesting system offers a solution to the power supply challenge for low-power sensors. However, In practical engineering, structural parameter uncertainties arise from manufacturing errors, environmental temperature fluctuations, and changes in material properties. Besides, due to inherent flaws in the structure and materials, asymmetry may occur in the potential well, which leads to the complexity of the dynamic behavior exhibited in energy harvesting system. In this paper, in order to investigate the dynamics of the asymmetric monostable piezoelectric energy harvesting system under an uncertain parameter, the orthogonal polynomial approximation method is applied to transform the stochastic system into an equivalent deterministic system. Then, the responses of the equivalent deterministic system are used to reveal the stochastic behavior of the uncertainty system. The results indicate that, when the uncertainty intensity of the cubic nonlinear coefficient is 0, an increase in the asymmetry level of the potential well leads to an increase in the output voltage. Increasing the other parameters, the output voltage is reduced. In addition, as the uncertainty intensity increases, the parameter intervals of chaotic response become wider and the energy harvesting efficiency decreases. Moreover, as the potential well asymmetry increases, the sensitivity of energy harvesting system to uncertain parameter increases.

Hopf bifurcation control of macroscopic traffic flow model considering vehicle braking effect.

Traffic congestion not only has a great impact on people's travel, but also increases energy consumption and air pollution. The control analysis of the macroscopic traffic flow model considering the vehicle braking effect is particularly important, reflecting the impact on the actual traffic flow density wave, so as to better solve the actual traffic problems. In this paper, based on a speed difference optimization speed model, the micro-macro-variables are transformed into a high-order continuous traffic flow model. Then, a random function considering the physical correlation of random components is added to the high-order continuous traffic flow model to establish a random traffic flow model that can reflect the uncertain behavior of traffic flow acceleration or deceleration. Based on this stochastic traffic model, the existence of Hopf bifurcation and bifurcation control of the traffic flow system model considering stochastic characteristics are derived by using Hopf bifurcation theorem. By Chebyshev polynomial approximation method, the stochastic problem of the system is transformed into the bifurcation control problem of its equivalent deterministic system. A feedback controller is designed to delay the occurrence of Hopf bifurcation and control the amplitude of the limit cycle. Without changing the equilibrium point of the system, the complete elimination of Hopf bifurcation can be achieved by controlling the amplitude of the limit cycle. That is, the feedback controller is used to modify the bifurcation characteristics of the system, such as the bifurcation appearing at the equilibrium point in the control system moves forward, moves backward or disappears, so as to achieve the effect of preventing or alleviating traffic congestion.

Stochastic Hopf\u2013Hopf bifurcation of two-species discrete coupling logistic system with symbiotic interaction

In this paper, stochastic Hopf–Hopf bifurcation of the discrete coupling logistic system with symbiotic interaction is investigated. Firstly, orthogonal polynomial approximation of discrete random function in the Hilbert spaces is applied to reduce the discrete coupling logistic system with random parameter to the deterministic equivalent system. Then, it is concluded that Hopf–Hopf bifurcation exists in the equivalent deterministic system according to the principle of algebraic criteria. Numerical simulations show that the bifurcation critical value varies with the intensity of random parameter, and Hopf–Hopf bifurcation and period-doubling bifurcation behavior exist. In particular, Hopf–Hopf bifurcation can be drift with the change of random intensity, and frequency locking phenomenon occurs in the stochastic system.

Uncertainty Quantification Spectral Technique for the Stochastic Point Reactor with Random Parameters

The stochastic point reactor with random parameters is considered in this work. The hybrid uncertain variations—noise and random parameters—are analyzed with the spectral techniques for the efficiency and high rates of convergence. The proposed hybrid technique enables one to derive an equivalent deterministic system that can be solved to get the mean solution and deviations due to each uncertainty. The contributions of different sources uncertainties can be decomposed and quantified. The deviations in the thermal hydraulics are also computed in the current work. Two model reactors are tested with the proposed technique and the comparisons show the advantages and efficiency compared with the other techniques.

Response analysis of the nonlinear vibration energy harvester with an uncertain parameter

In this paper, the Chebyshev polynomial approximation is firstly utilized to analyze the dynamical characteristics of the nonlinear vibration energy harvester with an uncertain parameter. First, the stochastic energy harvester is transformed into a high-dimensional equivalent deterministic system by the Chebyshev polynomial approximation. And the ensemble mean response of the stochastic energy harvester is introduced to discuss the stochastic response. Then, the effectiveness of the approximation method is verified by numerical results. Furthermore, the bifurcation property of the displacement and voltage is analyzed, which is also consistent with the results derived by the top Lyapunov exponent. It is found that random factor can induce the appearance of multi-periodic phenomena and lead to appear the behavior of the periodic bifurcation. The strong random factor induces the fluctuation of the output voltage. In addition, the existence of the random factor greatly influences the property of the sub-harmonics and super-harmonics of the spectrum. Overall, the response mechanism of the nonlinear vibration energy harvester with an uncertain parameter is revealed.

Study on the Influence of Stochastic Parameters of HOPF Bifurcation System in Coastal Management

Qiao, Y.; Huang, Y.-S., and Li, J.-M., 2019. Study on the influence of stochastic parameters of HOPF bifurcation system in coastal management. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 696–700. Coconut Creek (Florida), ISSN 0749-0208.Shallow water bathymetric data is an important foundation for scientific research and coastal management.HOPF bifurcation, as a complex nonlinear phenomenon, has been widely studied in ocean and other scientific and technological fields.In this paper, the HOPF bifurcation of stochastic Lorenz-family systems with random parameter is addressed. In the analysis, the stochastic Lorenz-family system is firstly transformed into an equivalent deterministic system by the Chebyshev polynomial approximation, so that the HOPF bifurcation of stochastic system can be reduced into that of the equivalent deterministic system. Combined with the definition of HOPF bifurcation and some mathematical analysis tools, the critical value of HOPF bifurcation is obtained and the influence of the random parameter on HOPF bifurcation in stochastic Lorenz-family system is analyzed. Atlast these results are verified by numerical simulation.

Stochastic Dynamic Load Identification on an Uncertain Structure With Correlated System Parameters

Abstract A stochastic dynamic load identification algorithm is proposed for an uncertain dynamic system with correlated random system parameters. The stochastic Green's function is adopted to establish the relationship between the Gaussian excitation and the response. The Green's function is approximated by the second-order perturbation method, and orthogonal polynomial chaos bases are adopted to replace the corresponding bases in the Tayler series. The stochastic system responses and the stochastic forces are then represented by the polynomial chaos expansion (PCE) and the Karhunen–Loève expansion (KLE), respectively. A unified probabilistic framework for the stochastic dynamic problem is formulated based on the PCE. The stochastic load identification problem of an uncertain dynamic system is then transformed into a stochastic load identification problem of an equivalent deterministic system with the orthogonality of the PCE. Numerical simulations and experimental studies with a cantilever beam under a concentrate stochastic force are conducted to estimate the statistical characteristics of the stochastic load from the stochastic structural response samples. Results show that the proposed method has good accuracy in the identification of force's statistics when the level of uncertainty in the system parameters is not small. Large errors in the identified statistics may occur when the correlation in the random system parameters is neglected. Different correlation lengths for the random system parameters are investigated to show the effectiveness and accuracy of the proposed method.

The Impact of Stochasticity and Its Control on a Model of the Inflammatory Response

The dysregulation of inflammation, normally a self-limited response that initiates healing, is a critical component of many diseases. Treatment of inflammatory disease is hampered by an incomplete understanding of the complexities underlying the inflammatory response, motivating the application of systems and computational biology techniques in an effort to decipher this complexity and ultimately improve therapy. Many mathematical models of inflammation are based on systems of deterministic equations that do not account for the biological noise inherent at multiple scales, and consequently the effect of such noise in regulating inflammatory responses has not been studied widely. In this work, noise was added to a deterministic system of the inflammatory response in order to account for biological stochasticity. Our results demonstrate that the inflammatory response is highly dependent on the balance between the concentration of the pathogen and the level of biological noise introduced to the inflammatory network. In cases where the pro- and anti-inflammatory arms of the response do not mount the appropriate defense to the inflammatory stimulus, inflammation transitions to a different state compared to cases in which pro- and anti-inflammatory agents are elaborated adequately and in a timely manner. In this regard, our results show that noise can be both beneficial and detrimental for the inflammatory endpoint. By evaluating the parametric sensitivity of noise characteristics, we suggest that efficiency of inflammatory responses can be controlled. Interestingly, the time period on which parametric intervention can be introduced efficiently in the inflammatory system can be also adjusted by controlling noise. These findings represent a novel understanding of inflammatory systems dynamics and the potential role of stochasticity thereon.

The suppression of random parameter on the boundary crisis of the smooth and discontinuous oscillator system

In this paper, the effect of random parameter on the boundary crisis of the smooth and discontinuous oscillator system is studied. First of all, we convert this stochastic system into a high-dimensional equivalent deterministic system by means of the orthogonal polynomial approximation. Afterward, the method of composite cell coordinate system is used to exhibit the global dynamical behavior of this system in different state spaces. Then, the attractors, basins of attraction and saddles of this system are obtained. We find that there exists interesting boundary crisis phenomenon in this system and the randomness of parameter has an obvious effect on it. Finally, we make a conclusion that the random parameter could suppress the boundary crisis of this system.

On the analysis of random replacement caches using static probabilistic timing methods for multi-path programs

Probabilistic hard real-time systems, based on hardware architectures that use a random replacement cache, provide a potential means of reducing the hardware over-provision required to accommodate pathological scenarios and the associated extremely rare, but excessively long, worst-case execution times that can occur in deterministic systems. Timing analysis for probabilistic hard real-time systems requires the provision of probabilistic worst-case execution time (pWCET) estimates. The pWCET distribution can be described as an exceedance function which gives an upper bound on the probability that the execution time of a task will exceed any given execution time budget on any particular run. This paper introduces a more effective static probabilistic timing analysis (SPTA) for multi-path programs. The analysis estimates the temporal contribution of an evict-on-miss, random replacement cache to the pWCET distribution of multi-path programs. The analysis uses a conservative join function that provides a proper over-approximation of the possible cache contents and the pWCET distribution on path convergence, irrespective of the actual path followed during execution. Simple program transformations are introduced that reduce the impact of path indeterminism while ensuring sound pWCET estimates. Evaluation shows that the proposed method is efficient at capturing locality in the cache, and substantially outperforms the only prior approach to SPTA for multi-path programs based on path merging. The evaluation results show incomparability with analysis for an equivalent deterministic system using an LRU cache. For some benchmarks the performance of LRU is better, while for others, the new analysis techniques show that random replacement has provably better performance.

Period-Doubling Bifurcation of Stochastic Fractional-Order Duffing System via Chebyshev Polynomial Approximation

Fractional-order calculus is more competent than integer-order one when modeling systems with properties of nonlocality and memory effect. And many real world problems related to uncertainties can be modeled with stochastic fractional-order systems with random parameters. Therefore, it is necessary to analyze the dynamical behaviors in those systems concerning both memory and uncertainties. The period-doubling bifurcation of stochastic fractional-order Duffing (SFOD for short) system with a bounded random parameter subject to harmonic excitation is studied in this paper. Firstly, Chebyshev polynomial approximation in conjunction with the predictor-corrector approach is used to numerically solve the SFOD system that can be reduced to the equivalent deterministic system. Then, the global and local analysis of period-doubling bifurcation are presented, respectively. It is shown that both the fractional-order and the intensity of the random parameter can be taken as bifurcation parameters, which are peculiar to the stochastic fractional-order system, comparing with the stochastic integer-order system or the deterministic fractional-order system. Moreover, the Chebyshev polynomial approximation is proved to be an effective approach for studying the period-doubling bifurcation of the SFOD system.

Function projective synchronization between integer-order and stochastic fractional-order nonlinear systems

In this paper, the function projective synchronization between integer-order and stochastic fractional-order nonlinear systems is investigated. Firstly, according to the stability theory of fractional-order systems and tracking control, a controller is designed. At the same time, based on the orthogonal polynomial approximation, the method of transforming stochastic error system into an equivalent deterministic system is given. Thus, the stability of the stochastic error system can be analyzed through its equivalent deterministic one. Finally, to demonstrate the effectiveness of the proposed scheme, the function projective synchronization between integer-order Lorenz system and stochastic fractional-order Chen system is studied.

Stochastic stability analysis and [formula omitted]-gain controller design for positive Markov jump systems with time-varying delays

This paper is concerned with stochastic stability analysis and L∞-gain (also called L∞-induced norm) controller design for positive Markov jump systems (MJSs) in continuous-time context by means of a linear programming (LP) technique. Firstly, by introducing an equivalent deterministic positive continuous-time linear system, we propose necessary and sufficient conditions of stochastic stability with a prescribed L∞-gain performance in LP form for the underlying system. Then based on the obtained results, an effective method for designing a desired L∞-gain controller is established. All the proposed conditions are solvable in terms of LP with additional parameters. Finally, a numerical example is given to illustrate the effectiveness of the present method.

Dynamical Polynomial Chaos Expansions and Long Time Evolution of Differential Equations with Random Forcing

Polynomial chaos expansions (PCE) allow us to propagate uncertainties in the coefficients of differential equations to the statistics of their solutions. Their main advantage is that they replace stochastic equations by systems of deterministic equations. Their main challenge is that the computational cost becomes prohibitive when the dimension of the parameters modeling the stochasticity is even moderately large. We propose a generalization of the PCE framework that allows us to keep this dimension as small as possible in favorable situations. For instance, in the setting of stochastic differential equations (SDEs) with Markov random forcing, we expect the future evolution to depend on the present solution and the future stochastic variables. We present a restart procedure that precisely allows PCE to depend only on that information. The computational difficulty then becomes the construction of orthogonal polynomials for dynamically evolving measures. We present theoretical results of convergence for our...

Hopf Bifurcation of Compound Stochastic van der Pol System

Hopf bifurcation analysis for compound stochastic van der Pol system with a bound random parameter and Gaussian white noise is investigated in this paper. By the Karhunen-Loeve (K-L) expansion and the orthogonal polynomial approximation, the equivalent deterministic van der Pol system can be deduced. Based on the bifurcation theory of nonlinear deterministic system, the critical value of bifurcation parameter is obtained and the influence of random strengthδand noise intensityσon stochastic Hopf bifurcation in compound stochastic system is discussed. At last we found that increasedδcan relocate the critical value of bifurcation parameter forward while increasedσmakes it backward and the influence ofδis more sensitive thanσ. The results are verified by numerical simulations.

随机参数作用下参激双势阱Duffing系统的随机动力学行为分析

Based on the orthogonal polynomial approximation theory, the stochastic dynamical behaviors of double-well Duffing systems under random parametric excitations were investigated. Firstly, the complex dynamical behaviors of deterministic Duffing systems were studied by means of the Poincaré sections. Then, the Duffing systems with random stiffnesses and damping parameters were reduced to equivalent deterministic expanded-order systems, and the effectiveness of this approximation method was proved. Thus, the ensemble-mean responses of the equivalent systems were applied to reveal the stochastic dynamical properties and the effects of the random variable intensity on the double-well Duffing systems. The numerical simulation results indicate that, in the case of coexistent attractors, the double-well Duffing system with random stiffness parameters has the similar stable dynamical behaviors to those in deterministic cases. However, for the Duffing system with random damping parameters, during the increase of the random variable intensity, the bifurcation phenomena occur to some coexistent attractors.

Hopf Bifurcation Analysis for a Stochastic Discrete-Time Hyperchaotic System

The dynamics of a discrete-time hyperchaotic system and the amplitude control of Hopf bifurcation for a stochastic discrete-time hyperchaotic system are investigated in this paper. Numerical simulations are presented to exhibit the complex dynamical behaviors in the discrete-time hyperchaotic system. Furthermore, the stochastic discrete-time hyperchaotic system with random parameters is transformed into its equivalent deterministic system with the orthogonal polynomial theory of discrete random function. In addition, the dynamical features of the discrete-time hyperchaotic system with random disturbances are obtained through its equivalent deterministic system. By using the Hopf bifurcation conditions of the deterministic discrete-time system, the specific conditions for the existence of Hopf bifurcation in the equivalent deterministic system are derived. And the amplitude control with random intensity is discussed in detail. Finally, the feasibility of the control method is demonstrated by numerical simulations.

Adaptive Wick--Malliavin Approximation to Nonlinear SPDEs with Discrete Random Variables

We propose an adaptive Wick--Malliavin (WM) expansion in terms of the Malliavin derivative of order $Q$ to simplify the propagator of general polynomial chaos (gPC) of order $P$ (a system of deterministic equations for the coefficients of gPC) and to control the error growth with respect to time. Specifically, we demonstrate the effectiveness of the WM method by solving a stochastic reaction equation and a Burgers equation with several discrete random variables. Exponential convergence is shown numerically with respect to $Q$ when $Q \geq P-1$. We also analyze the computational complexity of the WM method and identify a significant speedup with respect to gPC, especially in high dimensions.

Stochastic Hopf bifurcation analysis in a stochastic lagged logistic discrete-time system with Poisson distribution coefficient

In this paper, stochastic Hopf bifurcation behavior of a stochastic lagged logistic system is investigated. Firstly, the stochastic lagged logistic system with random parameter is approximately transformed as its equivalent deterministic system by the orthogonal polynomial approximation of discrete random function in the Hilbert spaces. Then, according to the bifurcation conditions of deterministic discrete system, Hopf bifurcation is existent in the equivalent deterministic system by mathematical analysis. Moreover, the direction and stability of its bifurcation is discussed by the normal form and center manifold theory. Finally, we verify the influence for the different random intensity on bifurcation critical value by numerical simulations and find that Hopf bifurcation phenomena under the influence of random intensity happen to drift.

Dynamic load identification for stochastic structures based on Gegenbauer polynomial approximation and regularization method

Based on the Gegenbauer polynomial expansion theory and regularization method, an analytical method is proposed to identify dynamic loads acting on stochastic structures. Dynamic loads are expressed as functions of time and random parameters in time domain and the forward model of dynamic load identification is established through the discretized convolution integral of loads and the corresponding unit-pulse response functions of system. Random parameters are approximated through the random variables with λ-probability density function (PDFs) or their derivative PDFs. For this kind of random variables, Gegenbauer polynomial expansion is the unique correct choice to transform the problem of load identification for a stochastic structure into its equivalent deterministic system. Just via its equivalent deterministic system, the load identification problem of a stochastic structure can be solved by any available deterministic methods. With measured responses containing noise, the improved regularization operator is adopted to overcome the ill-posedness of load reconstruction and to obtain the stable and approximate solutions of certain inverse problems and the valid assessments of the statistics of identified loads. Numerical simulations demonstrate that with regard to stochastic structures, the identification and assessment of dynamic loads are achieved steadily and effectively by the presented method.