一类新Willis环脑动脉瘤系统的构造及最优控制

本文基于6-氨基乙酸减小血流速度及缓冲血流波动的性质,在6-氨基乙酸的作用下,构建了一个新的脑动脉瘤数学模型.该模型很好地体现了6-氨基乙酸、血流速度及血流速度变化率三者之间的相互作用关系.鉴于脑动脉瘤的医疗费用颇高及破裂后的死亡率较高,从而有必要从数学的角度研究该模型的最优控制,以致在一定条件下花费最小且医疗效果最佳.本文首先证明了该模型最优控制的存在性;其次通过构造Lagrangian函数及运用最大值原理,证明了最优控制的唯一性.从理论上得到Willis环脑动脉瘤内血流波动最小的条件,这为预防脑动脉瘤的破裂提供了理论依据..     

一类离散捕食-食饵模型的混沌跟踪控制

针对一类离散捕食食饵系统,采用Lyapunov指数方法、绘制系统分岔图和混沌吸引子等方式验证了混沌现象的存在.为了消除系统的混沌现象,根据控制理论的轨迹跟踪控制原理,设计混沌跟踪控制器将系统控制到任意给定的期望周期轨道上,达到了稳定种群的目的.仿真分析表明了所设计控制器的有效性.     

Willis环状脑动脉瘤生物数学模型的周期与概周期解

本文运用构造Liapunov函数的方法,在一定条件下,证明了Willis环状脑动脉瘤生物数学模型存在唯一的周期与概周期解．     

长爪沙鼠脑底动脉Willis环变异缺失遗传特性分析及脑缺血模型高发群体的初步培育

目的研究长爪沙鼠脑底动脉Willis环遗传特性,并定向培育脑缺血高发种群。方法通过对5代定向培育的长爪沙鼠高发群动物共398只动物的右侧颈总动脉结扎模型进行观察,比较长爪沙鼠亲代与子代间脑底动脉Willis环的变异缺失类型,探求其遗传特性,并根据该遗传特性将Willis环后交通支缺失且前交通支缺失或细小的同类型的长爪沙鼠父母所生的子代雌雄个体配对繁殖,定向培育长爪沙鼠半脑缺血模型高发生率种群。结果当双亲的Willis环类型一致时,其子代大部分与其父母一致;而当双亲的Willis环类型不一致时,Willis环前交通支与母亲一致率为60.4%,前交通支与父亲的一致率为48.2%,两者的差异有显著性意义（P=0.015）。长爪沙鼠半脑缺血模型高发生率种群定向培育5代后,行单侧颈总动脉结扎时,一侧脑缺血造模成功率由F1代的40%提高到F5代的75%。结论长爪沙鼠脑底动脉Willis环变异有明显的遗传性,初步培育出了长爪沙鼠半脑缺血模型高发生率种群。     

射击过程中主要生理特征的研究

八名射击运动员(手枪2人,步枪6人),在实弹射击训练中,连续同步地记录了脑电、心电、心率、阻抗呼吸波。同时记录了受试者持枪稳定的程度。 分析表明,射击环数是五项独立无关的指标综合作用的结果。本文首次对射击时生理指标和射击环数进行系统辨识建模分析,求出每个人的系统参数_i值。找出了每人射击时最佳生理参数,为指导射击练习和反馈训练,提供了客观生理依据。 本文设计的激光光点录相方法,可反映持枪稳定的程度,简单、方便,可在射击训练中采用。     

时滞援助的两抑制性突触耦合的Chay 神经元的同步

一对抑制性突触耦合的混沌Chay神经元的同步模式被研究。结果表明当耦合强度超过临界值时,两抑制耦合的混沌Chay神经元能达到反相的同步。与此同时,两混沌的神经元变为周期而不是原来的混沌运动。然而,如果考虑耦合神经元信息的传导时滞,在有效的时滞下,两个耦合神经元的在相簇同步能增加。在相簇同步窗口的大小随着耦合强度的增加而增加。此结果对于我们理解神经元集群的运动是一个指导。     

显微外科手术治疗破裂前循环脑动脉瘤的临床研究

目的:旨在进一步提高手术治疗破裂的前循环脑动脉瘤的效果.方法:回顾性分析显微手术治疗的56例破裂前循环脑动脉瘤患者的临床资料.结果:56例病人,共61个脑动脉瘤,其中45枚行动脉瘤颈夹闭,6枚夹闭瘤颈后切除瘤体,另10枚动脉瘤予以瘤壁包裹加固术.术后第3天发生血管闭塞1例,遗留肢体偏瘫.25例随访1个月～5年,恢复良好17例,中残或重残2例,死亡2例.结论:显微手术治疗破裂的脑动脉瘤,不仅有效地防止再出血,而且有利于脑血管痉挛的防治.术后扩充血容量,提升血压,扩张血管对防治脑血管痉挛具有相当好的疗效.     

具有共生作用两种群离散耦合Logistic模型混沌控制

针对具有共生作用的离散耦合Logistic模型,首先采用Lyapunov指数方法验证了混沌现象的存在.然后详细地分析了系统随参数变化的分岔图,发现了系统中存在更复杂的现象.最后应用混沌跟踪控制方法控制系统的混沌现象,使得种群稳定到正不动点轨道上,消除了种群中存在的混沌现象.仿真结果验证了控制方法的有效性.     

Hindmarsh-Rose 神经网络的混沌同步

研究了通过特殊构造的非线性函数耦合连接的神经网络的混沌同步问题。在发展基于稳定性准则的混沌同步方法的基础上,给出了计算同步稳定性的误差发展方程,当耦合强度取参考值时,可实现稳定的混沌同步而不需要计算最大条件Lyapunov指数去判定是否稳定。通过对按照完全连接形式构成的Hindmarsh-Rose神经网络的数值模拟,显示可仅从两个耦合神经的耦合强度的稳定性范围预期到许多耦合神经实现同步的稳定性范围。该方法在噪声影响下,对实现神经元的混沌同步仍具有较强的鲁棒性。此外发现随着耦合神经数的增加,满足同步稳定性的耦合强度减小,与耦合神经的数量成反比。     

周期激励下FHN模型的两种多峰分布放电节律

文章揭示了外界周期脉冲激励下神经元系统产生的随机整数倍和混沌多峰放电节律的关系.随机节律统计直方图呈多峰分布、峰值指数衰减、不可预报且复杂度接近1;混沌节律统计直方图呈不同的多峰分布,峰值非指数衰减、有一定的可预报性且复杂度小于1.混沌节律在激励脉冲周期小于系统内在周期且刺激强度较大时产生,参数范围较小;而随机节律在激励脉冲周期大于系统内在周期且脉冲刺激强度小时,可与随机因素共同作用而产生,产生的参数范围较大.上述结果揭示了两类节律的动力学特性,为区分两类节律提供了实用指标.     

Information flow in heterogeneously interacting systems

Motivated by studies on the dynamics of heterogeneously interacting systems in neocortical neural networks, we studied heterogeneously-coupled chaotic systems. We used information-theoretic measures to investigate directions of information flow in heterogeneously coupled Rössler systems, which we selected as a typical chaotic system. In bi-directionally coupled systems, spontaneous and irregular switchings of the phase difference between two chaotic oscillators were observed. The direction of information transmission spontaneously switched in an intermittent manner, depending on the phase difference between the two systems. When two further oscillatory inputs are added to the coupled systems, this system dynamically selects one of the two inputs by synchronizing, selection depending on the internal phase differences between the two systems. These results indicate that the effective direction of information transmission dynamically changes, induced by a switching of phase differences between the two systems.     

Why do evolutionary systems stick to the edge of chaos

Summary  The long-term behaviour of dynamic systems can be classified in two different regimes, regular or chaotic, depending on the values of the control parameters, which are kept constant during the time evolution. Starting from slightly different initial conditions, a regular system converges to the same final trajectory, whereas a chaotic system follows two distinct trajectories exponentially diverging from each other. In spite of these differences, regular and chaotic systems share a common property: both arrive exponentially fast to their final destiny, becoming trapped there. In both cases one has finite transient times. This is not a profitable property in what concerns evolutionary strategies, where the eternal search for new forms, better than the current one, is imperative. That is why evolutionary dynamic systems tend to tune themselves in very particular situations in between regular and chaotic regimes. These particular situations present eternal transients, and the system actually never reaches its final destiny, preserving diversity. This feature allows the system to visit other regions of the space of possibilities, not only the tiny region covered by its final attractor.     

Numerically evaluated functional equivalence between chaotic dynamics in neural networks and cellular automata under totalistic rules

Chaotic dynamics in a recurrent neural network model and in two-dimensional cellular automata, where both have finite but large degrees of freedom, are investigated from the viewpoint of harnessing chaos and are applied to motion control to indicate that both have potential capabilities for complex function control by simple rule(s). An important point is that chaotic dynamics generated in these two systems give us autonomous complex pattern dynamics itinerating through intermediate state points between embedded patterns (attractors) in high-dimensional state space. An application of these chaotic dynamics to complex controlling is proposed based on an idea that with the use of simple adaptive switching between a weakly chaotic regime and a strongly chaotic regime, complex problems can be solved. As an actual example, a two-dimensional maze, where it should be noted that the spatial structure of the maze is one of typical ill-posed problems, is solved with the use of chaos in both systems. Our computer simulations show that the success rate over 300 trials is much better, at least, than that of a random number generator. Our functional simulations indicate that both systems are almost equivalent from the viewpoint of functional aspects based on our idea, harnessing of chaos.     

Complex Generalized Synchronization and Parameter Identification of Nonidentical Nonlinear Complex Systems

In this paper, generalized synchronization (GS) is extended from real space to complex space, resulting in a new synchronization scheme, complex generalized synchronization (CGS). Based on Lyapunov stability theory, an adaptive controller and parameter update laws are designed to realize CGS and parameter identification of two nonidentical chaotic (hyperchaotic) complex systems with respect to a given complex map vector. This scheme is applied to synchronize a memristor-based hyperchaotic complex Lü system and a memristor-based chaotic complex Lorenz system, a chaotic complex Chen system and a memristor-based chaotic complex Lorenz system, as well as a memristor-based hyperchaotic complex Lü system and a chaotic complex Lü system with fully unknown parameters. The corresponding numerical simulations illustrate the feasibility and effectiveness of the proposed scheme.     

Control of Pyragas Applied to a Coupled System with Unstable Periodic Orbits

We apply a Pyragas-type control in order to synchronize the solutions of a glycolytic model that exhibits an aperiodic behavior. This delay control is used to stabilize the orbits of ordinary differential nonlinear equations systems. Inspired by several works that studied the chaotic behavior of diverse systems for the enzymatic reactions in the presence of feedbacks, the control to two of these models is analyzed.     

Beyond Benford's Law: Distinguishing Noise from Chaos

Determinism and randomness are two inherent aspects of all physical processes. Time series from chaotic systems share several features identical with those generated from stochastic processes, which makes them almost undistinguishable. In this paper, a new method based on Benford''s law is designed in order to distinguish noise from chaos by only information from the first digit of considered series. By applying this method to discrete data, we confirm that chaotic data indeed can be distinguished from noise data, quantitatively and clearly.     

Parameter Estimation of Fractional-Order Chaotic Systems by Using Quantum Parallel Particle Swarm Optimization Algorithm

Parameter estimation for fractional-order chaotic systems is an important issue in fractional-order chaotic control and synchronization and could be essentially formulated as a multidimensional optimization problem. A novel algorithm called quantum parallel particle swarm optimization (QPPSO) is proposed to solve the parameter estimation for fractional-order chaotic systems. The parallel characteristic of quantum computing is used in QPPSO. This characteristic increases the calculation of each generation exponentially. The behavior of particles in quantum space is restrained by the quantum evolution equation, which consists of the current rotation angle, individual optimal quantum rotation angle, and global optimal quantum rotation angle. Numerical simulation based on several typical fractional-order systems and comparisons with some typical existing algorithms show the effectiveness and efficiency of the proposed algorithm.     

Chaotic stochasticity: a ubiquitous source of unpredictability in epidemics.

We address the question of whether or not childhood epidemics such as measles and chickenpox are chaotic, and argue that the best explanation of the observed unpredictability is that it is a manifestation of what we call chaotic stochasticity. Such chaos is driven and made permanent by the fluctuations from the mean field encountered in epidemics, or by extrinsic stochastic noise, and is dependent upon the existence of chaotic repellors in the mean field dynamics. Its existence is also a consequence of the near extinctions in the epidemic. For such systems, chaotic stochasticity is likely to be far more ubiquitous than the presence of deterministic chaotic attractors. It is likely to be a common phenomenon in biological dynamics.     

Analysis of Chaotic Resonance in Izhikevich Neuron Model

In stochastic resonance (SR), the presence of noise helps a nonlinear system amplify a weak (sub-threshold) signal. Chaotic resonance (CR) is a phenomenon similar to SR but without stochastic noise, which has been observed in neural systems. However, no study to date has investigated and compared the characteristics and performance of the signal responses of a spiking neural system in some chaotic states in CR. In this paper, we focus on the Izhikevich neuron model, which can reproduce major spike patterns that have been experimentally observed. We examine and classify the chaotic characteristics of this model by using Lyapunov exponents with a saltation matrix and Poincaré section methods in order to address the measurement challenge posed by the state-dependent jump in the resetting process. We found the existence of two distinctive states, a chaotic state involving primarily turbulent movement and an intermittent chaotic state. In order to assess the signal responses of CR in these classified states, we introduced an extended Izhikevich neuron model by considering weak periodic signals, and defined the cycle histogram of neuron spikes as well as the corresponding mutual correlation and information. Through computer simulations, we confirmed that both chaotic states in CR can sensitively respond to weak signals. Moreover, we found that the intermittent chaotic state exhibited a prompter response than the chaotic state with primarily turbulent movement.     

Can noise induce chaos?

An important component of the mathematical definition of chaos is sensitivity to initial conditions. Sensitivity to initial conditions is usually measured in a deterministic model by the dominant Lyapunov exponent (LE), with chaos indicated by a positive LE. The sensitivity measure has been extended to stochastic models; however, it is possible for the stochastic Lyapunov exponent (SLE) to be positive when the LE of the underlying deterministic model is negative, and vice versa. This occurs because the LE is a long-term average over the deterministic attractor while the SLE is the long-term average over the stationary probability distribution. The property of sensitivity to initial conditions, uniquely associated with chaotic dynamics in deterministic systems, is widespread in stochastic systems because of time spent near repelling invariant sets (such as unstable equilibria and unstable cycles). Such sensitivity is due to a mechanism fundamentally different from deterministic chaos. Positive SLE's should therefore not be viewed as a hallmark of chaos. We develop examples of ecological population models in which contradictory LE and SLE values lead to confusion about whether or not the population fluctuations are primarily the result of chaotic dynamics. We suggest that "chaos" should retain its deterministic definition in light of the origins and spirit of the topic in ecology. While a stochastic system cannot then strictly be chaotic, chaotic dynamics can be revealed in stochastic systems through the strong influence of underlying deterministic chaotic invariant sets.