Encoding Catalytic P Systems in π@

P systems are theoretical computing devices abstracted away from the biological architecture of the cell, introduced some years ago by Gheorghe Păun and now intensely studied. In the area of concurrent systems, process calculi have recently been applied and extended with similar aim, to simulate (and formalise) the behaviour of the cell. Although many common points can be found between the two approaches, no formal and exhaustive comparison has been carried out yet.π@ is a new calculus, strongly π-Calculus based, well-suited to easily encode biologically inspired process calculi. In this paper the encoding in π@ of one variant of P systems is proposed, thus allowing a better understanding of similarities between P systems and bio-inspired process calculi.     

Perfect Simulation of a Class of Stochastic Hybrid Systems with an Application to Peer to Peer Systems

In this paper we present a class of hybrid systems made of deterministic differential equations and random discrete jumps. We then show how to construct a simulation of such a stochastic hybrid system that provides perfect samples of its asymptotic behavior based on the extension to continuous state-space of coupling-from-the-past techniques introduced by Foss and Tweedie (Stoch Models 14:187–204, 1998) and using suitable envelope trajectories to tackle non-monotonicity. The applicability of the method is illustrated by showing how this framework can be used to model the Squirrel peer to peer system and by reporting a simulation study based on this approach. This paper provides both a framework on how to carry simulation based experimental studies of large and complex hybrid systems and its application in the Squirrel model demonstrating how versatile and powerful this approach can be over a typical example.

A Simple Calculus for Proteins and Cells

The use of process calculi to represent biological systems has led to the design of different calculi such as brane calculi [Luca Cardelli. Brane calculi. In CMSB, pages 257–278, 2004] and κ-calculus [Vincent Danos and Cosimo Laneve. Formal molecular biology. Theoritical Computer Science, 325(1):69–110, 2004]. Both have proved to be useful to model different types of biological systems.As an attempt to unify the two directions, we introduce the bioκ-calculus, a simple calculus for describing proteins and cells, in which bonds are represented by means of shared names and interactions are modelled at the domain level. Protein-protein interactions have to be at most binary and cell interactions have to fit with sort constraints.We define the semantics of bioκ-calculus, analyse its properties, and discuss its expressiveness by modelling two significant examples: a signalling pathway and a virus infection.     

Finite Horizon Minimax Optimal Control of Nonlinear Continuous Time Systems with Stochastic Uncertainty

This paper develops a general continuous-time stochastic framework for robustness analysis and robust control synthesis. We consider a stochastic minimax optimization problem for general stochastic uncertain systems. A general method is presented for converting problems of performance analysis or controller synthesis into unconstrained optimization problems.     

Fuzzy Optimal Control for Bilinear Stochastic Systems with Fuzzy Parameters

In this paper we consider the control problem for a class of partially observed bilinear stochastic systems with fuzzy parameters. Using Takagi–Sugeno fuzzy model, the problem is described by three sets of fuzzy stochastic differential equations: one for the state process, one for the observed process and one for the controller which is assumed to be driven by the observed process. With this formulation, the original stochastic control problem can be treated as a deterministic identification problem in which the controller parameters and the corresponding membership functions are the unknowns. Using a suitable performance index, we have developed a set of necessary conditions for determining the parameters of the controller and the corresponding membership functions. Finally, some numerical simulations are presented to illustrate the effectiveness of the proposed fuzzy control scheme.     

Membrane Computing and Brane Calculi (Some Personal Notes)

This is a very short discussion of similarities and dissimilarities between membrane computing and brane calculi, insisting mainly on some recent ideas of bridging the two areas of research (from this last point of view, the bibliography tries to be as complete as possible) and proving that, in fact, there is no real dissimilarity...     

生化网络的随机Petri网建模与分析

细胞的行为是随机性的,学习细胞中的随机性有助于理解细胞的组织,设计和进化。建立、确认和分析随机的生化网络模型是当前计算系统生物学领域的一个重要研究主题。当前,标准的Petri网模型已经成为生化网络模拟和定性分析的有力工具。尝试使用随机Petri网对生化网络进行建模与分析,简单描述了随机Petri网理论对标准Petri网的扩充,通过对二聚作用和肌动蛋白这两个典型例子的建模与演化模拟,介绍、论证了随机Petri网理论的新应用。     

系统生物学的分析与建模

要从系统水平上理解生物系统，必需建立一个新的框架．这个新的框架就是系统生物学，其目的在于在系统水平上阐述和理解生物系统．本文试图以系统控制学科的视点，评述系统生物学的分析与建模的主要思路和方法，以及系统生物学发展现状和可能的发展前景．     

带反脉冲的同质脉冲神经膜系统

脉冲神经膜系统是一种膜系统中吸收了脉冲神经网络特点的新型生物计算装置,具有强大的计算能力.带反脉冲的同质脉冲神经膜系统是使用了两种对象(称为脉冲和反脉冲)、且其中每个神经元具有相同规则集合的一种脉冲神经膜系统的变体.本文研究了无延迟规则和突触权值情况下的带反脉冲同质脉冲神经膜系统的计算通用性问题,证明了这种P系统无论是工作在产生模式,还是接收模式下都是计算通用的.本文解答了曾湘祥等人提出的关于是否存在无延迟规则的同质脉冲神经膜系统和如何移除突触权值的两个公开问题.     

Optimal Control of Switching Times in Switched Stochastic Systems

In this paper, we solve an optimal control problem for switched stochastic systems using calculus of variations, where the objective is to minimize a cost functional defined on the state and the switching times are the sole control variables. In particular, we focus on the problem in which a pre‐specified sequence of active subsystems is given. For one switching time case, the derivative of the cost functional with respect to the switching time is derived, which has an especially simple form and can be directly used in gradient descent algorithms to locate the optimal switching instant. Then, we propose an approach to deal with the problem with multi‐switching times case. Finally, two numerical examples are given, highlighting the viability and advantages of the proposed methodology.     

Comparison of Hybrid Systems and Fluid Stochastic Petri Nets

Hybrid Systems are modelsof interacting digital and continuous devices with applicationsin the control of aircraft, computers, or modern cars for instance.Concurrently, Fluid Stochastic Petri Nets (FSPNs) have been introducedas an extension of stochastic Petri nets to model physical systemsthat contain continuous fluid quantities controlled by discretelogic, as well as a means to deal with the possible state explosionof discrete models. Of course, an FSPN is by definition a hybridsystem but in this paper, we compare a general hybrid systemsformalism with FSPN formalism. The conclusion of this paper,suplemented by some examples, is that fluid stochastic Petrinets and hybrid system formalism can be converted from one tothe other. Although there is a price to pay for such a conversion,the conversion can transfer the solution methods from one tothe other and thus help the development of both formalisms.     

Mixture of Automatically- and Manually-controlled Vehicles in Intelligent Transport Systems

This paper presents a technology which allows for the existence of mixed traffic, as a first step towards intelligent transport systems. We begin by designing an automatic driving controller called the intelligent vehicle driving system (IVDS). This is a two-layer system: the higher layer analyzes the current scenario and infers the control objective that associates with a certain index function; the lower layer optimizes the function provided by the upper layer. IVDS only uses the measurement of a vehicle's speed and distance relative to the vehicle in front, together with measurements of the vehicle's own state. Consequently, the vehicles equipped with the IVDS can operate together with manually-controlled vehicles. Next, a mathematical rule-based model for human drivers is developed. This model attempts to mimic human driver's behavior in vehicle following and lane-changing. Finally, we examine the control performance of the proposed controller and the potential benefits of mixed traffic by implementing the human driver model and IVDS on an automated highway simulator.     

Translating Mobile Ambients into P Systems

We present a translation of the mobile ambients without communication and replication into P systems with mobile membranes. We introduce a set of developmental rules over membranes, and describe the correspondence between the behaviour of an ambient and the evolution of its translated membrane system. We give an operational correspondence result between the mobile ambients and P systems.     

非线性滞后It(?)随机系统的滞后无关均方渐近稳定性

研究非线性滞后Ito随机系统的滞后无关均方渐近稳定性,将关于线性时滞不等式的Halanay不等式推广到非线性情形,用Lyapunov函数和关于时滞随机系统的比较原理,得到了非线性滞后Ito随机系统滞后无关均方渐近稳定性的一些判据。     

Membrane Systems with Marked Membranes

Membrane computing is a biologically inspired computational paradigm. Motivated by brane calculi we investigate membrane systems which differ from conventional membrane systems by the following features: (1) biomolecules (proteins) can move through the regions of the systems, and can attach onto (and de-attach from) membranes, and (2) membranes can evolve depending on the attached molecules. The evolution of membranes is performed by using rules that are motivated by the operation of pinocytosis (the pino rule) and the operation of cellular dripping (the drip rule) that take place in living cells. We show that such membrane systems are computationally universal. We also show that if only the second feature is used then one can generate at least the family of Parikh images of the languages generated by programmed grammars without appearance checking (which contains non-semilinear sets of vectors). If, moreover, the use of pino/drip rules is non-cooperative (i.e., not dependent on the proteins attached to membranes), then one generates a family of sets of vectors that is strictly included in the family of semilinear sets of vectors. We also consider a number of decision problems concerning reachability of configurations and boundness.     

含结构参数摄动的线性离散系统的鲁棒约束方差状态估计

考虑状态矩阵中含不确定性的线性离散随机系统的鲁棒约束方差状态估计问题,即设计滤波增益,使摄动系统每个状态分量的估计误差方差的稳态值不大于各自预先给定值。给出了期望鲁棒滤波增益的存在条件及其解析表达式,并以数值算例说明了设计方法的直接性与有效性。     

Sampled-data based average consensus with measurement noises: convergence analysis and uncertainty principle

In this paper, sampled-data based average-consensus control is considered for networks consisting of continuous-time first-order integrator agents in a noisy distributed communication environment. The impact of the sampling size and the number of network nodes on the system performances is analyzed. The control input of each agent can only use information measured at the sampling instants from its neighborhood rather than the complete continuous process, and the measurements of its neighbors’ states are corrupted by random noises. By probability limit theory and the property of graph Laplacian matrix, it is shown that for a connected network, the static mean square error between the individual state and the average of the initial states of all agents can be made arbitrarily small, provided the sampling size is sufficiently small. Furthermore, by properly choosing the consensus gains, almost sure consensus can be achieved. It is worth pointing out that an uncertainty principle of Gaussian networks is obtained, which implies that in the case of white Gaussian noises, no matter what the sampling size is, the product of the steady-state and transient performance indices is always equal to or larger than a constant depending on the noise intensity, network topology and the number of network nodes.     

Evolution in Groups: A Genetic Algorithm Approach to Group Decision Support Systems

Certain tasks undertaken by groups using Group Decision Support Systems (GDSS) can be viewed as search problems. These tasks involve arriving at a solution or decision where the problem is complex enough to warrant the use of computerized decision support tools. For these types of GDSS tasks, we propose to model the information exchange and convergence toward a solution by the group as a simple genetic algorithm. The simple genetic algorithm is a generalized search technique that is based on the principles of evolution and natural selection. Simply put, the best points in the current population are more likely to be selected and combined through genetic operators to determine new points. We propose that groups using GDSS to address certain tasks behave like a simple genetic algorithm in the manner in which possible solutions are generated, enhanced and altered in attempting to reach a decision or consensus.