Control of random sensitivity Boolean networks via of Boolean functions

In this paper, we discuss how to transform the disordered phase into an ordered phase in random Boolean networks. To increase the effectiveness, a control scheme is proposed, which periodically freezes a fraction of the network based on the average sensitivity of Boolean functions of the nodes. Theoretical analysis is carried out to estimate the expected critical value of the fraction, and shows that the critical value is reduced using this scheme compared to that of randomly freezing a fraction of the nodes. Finally, the simulation is given for illustrating the effectiveness of the proposed method.     

Control of random Boolean networks via average sensitivity of Boolean functions

In this paper,we discuss how to transform the disordered phase into an ordered phase in random Boolean networks.To increase the effectiveness,a control scheme is proposed,which periodically freezes a fraction of the network based on the average sensitivity of Boolean functions of the nodes.Theoretical analysis is carried out to estimate the expected critical value of the fraction,and shows that the critical value is reduced using this scheme compared to that of randomly freezing a fraction of the nodes.Finally,the simulation is given for illustrating the effectiveness of the proposed method.     

Quasi Product on Boolean D-Posets

The pointwise multiplication on a full tribe and the product operation on an MV-algebra play a crucial role in the construction of a joint observable. In the present paper we introduce a quasi product operation on Boolean D-posets and describe its properties. Our quasi product generalizes product on MV-algebras and in some cases also t-norms. This work was supported by Science and Technology Assistance Agency under the contracts No. APVT-51-032002, APVV-0071-06, and by the grant Center of Excellence Physics of Information I/2/2005.     

An optimal control approach to probabilistic Boolean networks

External control of some genes in a genetic regulatory network is useful for avoiding undesirable states associated with some diseases. For this purpose, a number of stochastic optimal control approaches have been proposed. Probabilistic Boolean networks (PBNs) as powerful tools for modeling gene regulatory systems have attracted considerable attention in systems biology. In this paper, we deal with a problem of optimal intervention in a PBN with the help of the theory of discrete time Markov decision process. Specifically, we first formulate a control model for a PBN as a first passage model for discrete time Markov decision processes and then find, using a value iteration algorithm, optimal effective treatments with the minimal expected first passage time over the space of all possible treatments. In order to demonstrate the feasibility of our approach, an example is also displayed.     

Physical generation of random numbers using an asymmetrical Boolean network

Autonomous Boolean networks (ABNs) have been successfully applied to the generation of random number due to their complex nonlinear dynamics and convenient on-chip integration. Most of the ABNs used for random number generators show a symmetric topology, despite their oscillations dependent on the inconsistency of time delays along links. To address this issue, we suggest an asymmetrical autonomous Boolean network (aABN) and show numerically that it provides large amplitude oscillations by using equal time delays along links and the same logical gates. Experimental results show that the chaotic features of aABN are comparable to those of symmetric ABNs despite their being made of fewer nodes. Finally, we put forward a random number generator based on aABN and show that it generates the random numbers passing the NIST test suite at 100 Mbits/s. The unpredictability of the random numbers is analyzed by restarting the random number generator repeatedly. The aABN may replace symmetrical ABNs in many applications using fewer nodes and, in turn, reducing power consumption.     

Minimum energy pulse synthesis via the inverse scattering transform

This paper considers a variety of problems in the design of selective RF-pulses. We apply a formula of Zakharov and Manakov to directly relate the energy of an RF-envelope to the magnetization profile and certain auxiliary parameters used in the inverse scattering transform (IST) approach to RF-pulse synthesis. This allows a determination of the minimum possible energy for a given magnetization profile. We give an algorithm to construct both the minimum energy RF-envelope as well as any other envelope that produces a given magnetization profile. This includes an algorithm for solving the Gel'fand-Levitan-Marchenko equations with bound states. The SLR method is analyzed in terms of traditional scattering data, and shown to be a special (singular) case of the IST approach. RF-envelopes are computed for a variety of examples.     

Coherent spaces,Boolean rings and quantum gates

Coherent spaces spanned by a finite number of coherent states, are introduced. Their coherence properties are studied, using the Dirac contour representation. It is shown that the corresponding projectors resolve the identity, and that they transform into projectors of the same type, under displacement transformations, and also under time evolution. The set of these spaces, with the logical OR and AND operations is a distributive lattice, and with the logical XOR and AND operations is a Boolean ring (Stone’s formalism). Applications of this Boolean ring into classical CNOT gates with n$n$-ary variables, and also quantum CNOT gates with coherent states, are discussed.     

Minimum spanning trees for community detection

A simple deterministic algorithm for community detection is provided by using two rounds of minimum spanning trees. By comparing the first round minimum spanning tree (1st-MST) with the second round spanning tree (2nd-MST) of the network, communities are detected and their overlapping nodes are also identified. To generate the two MSTs, a distance matrix is defined and computed from the adjacent matrix of the network. Compared with the resistance matrix or the communicability matrix used in community detection in the literature, the proposed distance matrix is very simple in computation. The proposed algorithm is tested on real world social networks, graphs which are failed by the modularity maximization, and the LFR benchmark graphs for community detection.     

脉冲形成网络能量传递效率分析

采用无感陶瓷电容器作为储能介质,相邻电容器采用宽度为2 cm、厚度为0.2 cm的金属铝条相连接,设计了2.5Ω,200 ns低阻抗Blumlein型脉冲形成网络。理论计算表明,当频率等于2.5 MHz时,相邻电容器之间导线的高频电阻约为3.2 mΩ;基于Pspice仿真软件的模拟结果表明,设计的脉冲形成网络能量传递效率可以达到94.92%;实验研究结果表明,在2.6Ω负载条件下,该脉冲形成网络的电压传递效率约为93.53%,能量传递效率约为93.22%。实验结果与理论分析结果基本吻合。     

Cost and effect of pinning control for network synchronization

The problem of pinning control for the synchronization of complex dynamical networks is discussed in this paper. A cost function of the controlled network is defined by the feedback gain and the coupling strength of the network. An interesting result is that a lower cost is achieved by using the control scheme of pinning nodes with smaller degrees. Some strict mathematical analyses are presented for achieving a lower cost in the synchronization of different star-shaped networks. Numerical simulations on some non-regular complex networks generated by the Barabási--Albert model and various star-shaped networks are performed for verification and illustration.     

混沌的模糊神经网络逆系统控制

提出用Sugeno型的模糊推理神经网络建立混沌系统的逆系统模型,并采用逆系统方法进行混沌的控制.这种方法的特点是可以不必建立混沌系统的解析模型,通过模糊神经网络学习混沌系统的运动规律,通过学习获得的规律对混沌进行有效的控制,并且该控制方法可以控制混沌系统以一定精度跟踪连续变化的给定信号.理论分析及针对虫口模型和Henon模型仿真研究证明了该方法的有效性 关键词： 混沌 模糊神经网络 逆系统控制     

Synchronization in an array of coupled Boolean networks

This Letter presents an analytical study of synchronization in an array of coupled deterministic Boolean networks. A necessary and sufficient criterion for synchronization is established based on algebraic representations of logical dynamics in terms of the semi-tensor product of matrices. Some basic properties of a synchronized array of Boolean networks are then derived for the existence of transient states and the upper bound of the number of fixed points. Particularly, an interesting consequence indicates that a “large” mismatch between two coupled Boolean networks in the array may result in loss of synchrony in the entire system. Examples, including the Boolean model of coupled oscillations in the cell cycle, are given to illustrate the present results.     

非线性系统混沌运动的神经网络控制

设计前馈反传神经网络控制非线性系统混沌运动的新方法.根据扰动参数模型输入输出数据,按照非线性学习算法训练网络产生系统稳定所需的小扰动控制信号,去镇定混沌运动,使嵌入在混沌吸引子中的不稳定周期轨道回到稳定不动点上.Hnon映射数值仿真结果表明,这种方法控制非线性混沌系统响应速度快、控制精度高 关键词： 混沌控制 神经网络 吸引子 非线性     

混沌系统的遗传神经网络控制

提出遗传神经网络控制混沌新方法.将小扰动技术和周期控制技术结合起来，用遗传算法训练神经网络，使之成为混沌控制器.对Henon映射和Logistic映射的仿真结果说明控制器能产生小扰动控制序列信号，将混沌振荡转变成规则运动状态.该方法无需了解动态系统数学模型，具有一定抗噪声干扰能力，可将它推广应用到其他混沌系统的控制中. 关键词： 遗传算法 神经网络 混沌 周期控制     

基于径向小波神经网络的混沌系统鲁棒自适应反演控制

设计了一种具有自适应性和鲁棒性的反演控制律, 实现了对含有系统不确定性的类Rossler系统的控制. 首先通过小波神经网络辨识系统的非线性部分, 将系统转化为含有结构不确定性和参数不确定性的参数化模型; 然后, 对于系统中的参数不确定性, 设计自适应控制律, 在线估计未知参数; 对于系统中的结构不确定性, 设计鲁棒控制律, 使得系统具有鲁棒性. 最后, 通过仿真实现, 验证了以上控制方法的有效性.     

On the Criticality of Adaptive Boolean Network Robots

Systems poised at a dynamical critical regime, between order and disorder, have been shown capable of exhibiting complex dynamics that balance robustness to external perturbations and rich repertoires of responses to inputs. This property has been exploited in artificial network classifiers, and preliminary results have also been attained in the context of robots controlled by Boolean networks. In this work, we investigate the role of dynamical criticality in robots undergoing online adaptation, i.e., robots that adapt some of their internal parameters to improve a performance metric over time during their activity. We study the behavior of robots controlled by random Boolean networks, which are either adapted in their coupling with robot sensors and actuators or in their structure or both. We observe that robots controlled by critical random Boolean networks have higher average and maximum performance than that of robots controlled by ordered and disordered nets. Notably, in general, adaptation by change of couplings produces robots with slightly higher performance than those adapted by changing their structure. Moreover, we observe that when adapted in their structure, ordered networks tend to move to the critical dynamical regime. These results provide further support to the conjecture that critical regimes favor adaptation and indicate the advantage of calibrating robot control systems at dynamical critical states.     

能量异构的无线传感器网络加权无标度拓扑研究

针对无线传感器网络节点能耗不均和如何高效获得节点和边的负载问题,提出一种局域范围内能量异构的加权无标度拓扑演化模型.通过对节点能量与负载、能耗的关系建模,建立节点能量与点权和边权的联系,进而结合点权和加权模型给出网络的演化方式,推出点权、度和边权的幂率分布规律,最终根据网络获得的点权和边权来分析负载和能耗.仿真结果表明,提出的模型不仅能够准确计算点边的负载,而且缓解了无标度网络的节点能耗不均衡问题.     

Circular Harmonic Minimum Average Energy Filter for the Multi-Distorted Invariant Correlation

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Order or chaos in Boolean gene networks depends on the mean fraction of canalizing functions

We explore the connection between order/chaos in Boolean networks and the naturally occurring fraction of canalizing functions in such systems. This fraction turns out to give a very clear indication of whether the system possesses ordered or chaotic dynamics, as measured by Derrida plots, and also the degree of order when we compare different networks with the same number of vertices and edges. By studying also a wide distribution of indegrees in a network, we show that the mean probability of canalizing functions is a more reliable indicator of the type of dynamics for a finite network than the classical result on stability relating the bias to the mean indegree. Finally, we compare by direct simulations two biologically derived networks with networks of similar sizes but with power-law and Poisson distributions of indegrees, respectively. The biologically motivated networks are not more ordered than the latter, and in one case the biological network is even chaotic while the others are not.