动态贝叶斯网络结构搜索法辨识生物神经网络连接

准确辨识生物网络的功能性连接结构,对于从系统水平探明网络调控机制,具有重要意义。文中发展了一种基于最小描述准则(minimum description length,MDL)的动态贝叶斯网络结构搜索法(dynamical Bayesian network structure searching method,DBNSSM),用于对脉冲神经元网络(pulsed neural network,PNN)(一种人工构造的生物神经元网络)结构进行辨识,以获得其内部神经元间的功能性连接情况和相互作用强度。在网络结构辨识过程中,候选网络结构评分函数综合考虑以下两个因素:1)利用网络动态响应数据确定的网络结构似然度;2)网络结构的复杂度。以上两因素相互折中后,评分最小的网络结构,即为最优网络。网络结构选择过程采用遗传算法(genetic algorithm,GA),候选网络结构对应的邻接矩阵元素构成二进制染色体,交叉、变异后,经历有限代的进化选择,收敛于全局最优网络结构。最后,将DBNSSM应用于PNN产生的动态时间序列数据。仿真结果表明:该方法能够有效地利用网络响应数据,辨识出生物神经元网络结构,未来可进一步应用于体外培养生物神经网络结构的辨识。

电磁波测井双参数反演的2-D移动网格搜索法

针对轴对称三层地层模型 ,以两接收线圈的相位差和幅度比为附加条件 ,建立了反演地层介电常数和电导率的电磁波测井反演数学模型 .为减少计算量 ,加快收敛速度 ,正演采用了快速的半解析、半有限元的数值模式匹配法 .针对此类问题非线性算子方程无法显式表示的特点 ,反演采用了 2 -D移动网格搜索法 ,具有收敛快、收敛范围大 ,并具有一定的抗噪能力 .最后的数值模拟结果表明了方法的有效性 .

一种确定定位迭代过程初始位置的方法

为了获取目标位置的估计值，目前的计算方法主要有迭代法和网格搜索法。迭代法的主要缺点是迭代的初始位置较难确定，网格搜索法的主要缺点是计算量大。以最大似然估计（MLE）定位法为例，针对网格搜索法计算量大的问题，分别用两步网格搜索法和粒子群优化（PSO）搜索法寻求最大似然估计定位的全局最优解，显著减小了计算量。仿真实验以测向交叉定位法为例，说明两步网格搜索法和PSO搜索法计算量小，且PSO搜索法定位精度较高，通过搜索法得到的定位结果可以作为迭代法迭代的初始位置。

基于网络搜索对比法的旅游形象测量——以阿尔山为例

本文以阿尔山风景区为例,运用网络搜索对比法分别从阿尔山的旅游目的地类型、景点(区)关注度、典型活动(事物)三个方面进行了比较细致的测量分析,为测量对象综合整治旅游环境、提升旅游形象提供参考。

席位分配问题的数学模型

建立了席位分配问题的 3个数学模型 ,它们都是利用最小二乘思想描述分配中的公平性 ,利用网格搜索法进行求解 ,模型简洁明了 ,求解算法有效 ,大量的数值实验为模型的实际应用提供了不少有价值的结论 .

基于最小二乘支持向量机的复杂气象条件下绝缘子等值附盐密度的预测

以"光传感器输变电设备盐密在线监测系统"提供的数据为依据,建立了一种基于最小二乘支持向量机的智能预测模型,该模型以温度(T)、相对湿度(H)、风速(WV)、气压(P)、雨量(R)等五个变量为输入参数,等值附盐密度为输出参数。用网格搜索法对最小二乘支持向量机最优参数的进行自动选取,提高了预测的快速性和准确性。结果表明该模型预测的等值附盐密度与实测结果基本一致。

浅谈铁塔挖孔桩基础的优化设计

讨论了挖孔桩基础进行优化设计以求造价最低,建立了确定基础埋深和直径的优化数学模型,并利用网络搜索法找出最优解的基础埋深和直径。

Wind speed forecasting based on wavelet decomposition and wavelet neural networks optimized by the Cuckoo search algorithm

Wind speed forecasting is of great importance for wind farm management and plays an important role in grid integration. Wind speed is volatile in nature and therefore it is difficult to predict with a single model. In this study, three hybrid multi-step wind speed forecasting models are developed and compared — with each other and with earlier proposed wind speed forecasting models. The three models are based on wavelet decomposition(WD), the Cuckoo search(CS) optimization algorithm, and a wavelet neural network(WNN). They are referred to as CS-WD-ANN(artificial neural network), CS-WNN, and CS-WD-WNN, respectively. Wind speed data from two wind farms located in Shandong, eastern China, are used in this study. The simulation result indicates that CS-WD-WNN outperforms the other two models, with minimum statistical errors. Comparison with earlier models shows that CS-WD-WNN still performs best, with the smallest statistical errors. The employment of the CS optimization algorithm in the models shows improvement compared with the earlier models.

Routing Protocol Based on Grover’s Searching Algorithm for Mobile Ad-hoc Networks

In Mobile Ad-hoc Networks (MANETs), routing protocols directly affect various indices of network Quality of Service (QoS), so they play an important role in network performance. To address the drawbacks associated with traditional routing protocols in MANETs, such as poor anti-fading performance and slow convergence rate, for basic Dynamic Source Routing (DSR), we propose a new routing model based on Grover's searching algorithm. With this new routing model, each node maintains a node vector function, and all the nodes can obtain a node probability vector using Grover's algorithm, and then select an optimal routing according to node probability. Simulation results show that compared with DSR, this new routing protocol can effectively extend the network lifetime, as well as reduce the network delay and the number of routing hops. It can also significantly improve the anti-jamming capability of the network.

Present Tectonic Stress Field in the Capital Region of China Inverted from Focal Mechanisms of Small and Strong Earthquakes

A dense seismic network was installed in the capital region of China in recent years,which makes it possible to resolve the focal mechanisms of small earthquakes. We gathered large earthquake focal mechanisms from the last fifty years and moderate or small earthquake focal mechanisms from between 2002 and 2004,and calculated the present tectonic stress field of the capital region by the grid search method, which weighs different sized earthquakes and can improve the accuracy of the stress field inversion. The analysis of inversion results of different sub-regions shows that the azinuth of the maximum principal compressive stress axis is NE43°- 86° in the Beijing-Zhangjiakou-Datong area,NE38°-86° in the Tangshan area,and NE79°- 81° in the Xingtai area. Inversion results of this paper are similar to previous results,which proves the correctness of the approach. As revealed by the results,the stress field of the capital region is characterized by overall consistency and sub-regional differences. This study provides reference for earthquake mechanism explanation and geodynamics research.

Distributed Localization Algorithm for Wireless Sensor Network Based on Multidimensional Scaling and the Shortest Path Distance Correction

Sensor localization is crucial for the configuration and applications of wireless sensor network (WSN). A novel distributed localization algorithm, MDS-DC was proposed for wireless sensor network based on multidimensional scaling (MDS) and the shortest path distance correction. In MDS-DC, several local positioning regions with reasonable distribution were firstly constructed by an adaptive search algorithm, which ensures the mergence between the local relative maps of the adjacent local position regions and can reduce the number of common nodes in the network. Then, based on the relationships between the estimated distances and actual distances of anchors, the distance estimation vectors of sensors around anchors were corrected in each local positioning region. During the computations of the local relative coordinates, an iterative process, which is the combination of classical MDS algorithm and SMACOF algorithm, was applied. Finally, the global relative positions or absolute positions of sensors were obtained through merging the relative maps of all local positioning regions. Simulation results show that MDS-DC has better performances in positioning precision, energy efficiency and robustness to range error, which can meet the requirements of applications for sensor localization in WSN.

Network coding resources optimization with transmission delay constraint in multicast networks

Minimizing network coding resources of multicast networks,such as the number of coding nodes or links,has been proved to be NP-hard,and taking propagation delay into account makes the problem more complicated. To resolve this optimal problem,an integer encoding routing-based genetic algorithm( REGA) is presented to map the optimization problem into a genetic algorithm( GA)framework. Moreover,to speed up the search process of the algorithm,an efficient local search procedure which can reduce the searching space size is designed for searching the feasible solution.Compared with the binary link state encoding representation genetic algorithm( BLSGA),the chromosome length of REGA is shorter and just depends on the number of sinks. Simulation results show the advantages of the algorithm in terms of getting the optimal solution and algorithmic convergence speed.

Global optimization by small-world optimization algorithm based on social relationship network

A fast global convergence algorithm, small-world optimization (SWO), was designed to solve the global optimization problems, which was inspired from small-world theory and six degrees of separation principle in sociology. Firstly, the solution space was organized into a small-world network model based on social relationship network. Secondly, a simple search strategy was adopted to navigate into this network in order to realize the optimization. In SWO, the two operators for searching the short-range contacts and long-range contacts in small-world network were corresponding to the exploitation and exploration, which have been revealed as the common features in many intelligent algorithms. The proposed algorithm was validated via popular benchmark functions and engineering problems. And also the impacts of parameters were studied. The simulation results indicate that because of the small-world theory, it is suitable for heuristic methods to search targets efficiently in this constructed small-world network model. It is not easy for each test mail to fall into a local trap by shifting into two mapping spaces in order to accelerate the convergence speed. Compared with some classical algorithms, SWO is inherited with optimal features and outstanding in convergence speed. Thus, the algorithm can be considered as a good alternative to solve global optimization problems.

Power loss reduction of distribution systems using BFO based optimal reconfiguration along with DG and shunt capacitor placement simultaneously in fuzzy framework

In distribution systems,network reconfiguration and capacitor placement are commonly used to diminish power losses and keep voltage profiles within acceptable limits.Moreover,the problem of DG allocation and sizing is great important.In this work,a combination of a fuzzy multi-objective approach and bacterial foraging optimization(BFO) as a meta-heuristic algorithm is used to solve the simultaneous reconfiguration and optimal sizing of DGs and shunt capacitors in a distribution system.Each objective is transferred into fuzzy domain using its membership function.Then,the overall fuzzy satisfaction function is formed and considered a fitness function inasmuch as the value of this function has to be maximized to gain the optimal solution.The numerical results show that the presented algorithm improves the performance much more than other meta-heuristic algorithms.Simulation results found that simultaneous reconfiguration with DG and shunt capacitors allocation(case 5) has 77.41%,42.15%,and 56.14%improvements in power loss reduction,load balancing,and voltage profile indices,respectively in 33-bus test system.This result found 87.27%,35.82%,and 54.34%improvements of mentioned indices respectively for 69-bus system.

On Minimizing Delay with Probabilistic Splitting of Traffic Flow in Heterogeneous Wireless Networks

In the paper,we propose a framework to investigate how to effectively perform traffic flow splitting in heterogeneous wireless networks from a queue point.The average packet delay in heterogeneous wireless networks is derived in a probabilistic manner.The basic idea can be understood via treating the integrated heterogeneous wireless networks as different coupled and parallel queuing systems.The integrated network performance can approach that of one queue with maximal the multiplexing gain.For the purpose of illustrating the effectively of our proposed model,the Cellular/WLAN interworking is exploited.To minimize the average delay,a heuristic search algorithm is used to get the optimal probability of splitting traffic flow.Further,a Markov process is applied to evaluate the performance of the proposed scheme and compare with that of selecting the best network to access in terms of packet mean delay and blocking probability.Numerical results illustrate our proposed framework is effective and the flow splitting transmission can obtain more performance gain in heterogeneous wireless networks.

A Location-Based Content Search Approach in Hybrid Delay Tolerant Networks

In Delay Tolerant Networks （DTNs）, the offiine users can, through the encountering nodes, use the specific peer-to-peer message routing approach to deliver messages to the destination. Thus, it solves the problem that users have the demands to deliver messages while they are temporarily not able to connect to the Internet. Therefore, by the characteristics of DTNs, people who are not online can still query some location based information, with the help of users using the same service in the nearby area. In this paper, we proposed a location-based content search approach. Based on the concept of three-tier area and hybrid node types, we presented four strategies to solve the query problem, namely, Data Replication, Query Replication, Data Reply, and Data Synchronization strategies. Especially we proposed a Message Queue Selection algorithm for message transferring. The priority concept is set associated with every message such that the most ＂important＂ one could be sent first. In this way, it can increase the query success ratio and reduce the query delay time. Finally, we evaluated our approach, and compared with other routing schemes. The simulation results showed that our proposed approach had better query efficiency and shorter delay.

Integrating Tabu Search in Particle Swarm Optimization for the Frequency Assignment Problem

In this paper, we address one of the issues in the frequency assignment problem for cellular mobile networks in which we intend to minimize the interference levels when assigning frequencies from a limited frequency spectrum. In order to satisfy the increasing demand in such cellular mobile networks, we use a hybrid approach consisting of a Particle Swarm Optimization(PSO) combined with a Tabu Search(TS) algorithm. This approach takes both advantages of PSO efficiency in global optimization and TS in avoiding the premature convergence that would lead PSO to stagnate in a local minimum. Moreover, we propose a new efficient, simple, and inexpensive model for storing and evaluating solution's assignment. The purpose of this model reduces the solution's storage volume as well as the computations required to evaluate thesesolutions in comparison with the classical model. Our simulation results on the most known benchmarking instances prove the effectiveness of our proposed algorithm in comparison with previous related works in terms of convergence rate, the number of iterations, the solution storage volume and the running time required to converge to the optimal solution.

Orientation in Social Networks

Stanley Milgram's small world experiment presents "six degrees of separation" of our world.One phenomenon of the experiment still puzzling us is that how individuals operating with the social network information with their characteristics can be very adept at finding the short chains. The previous works on this issue focus whether on the methods of navigation in a given network structure,or on the effects of additional information to the searching process. In this paper, the authors emphasize that the growth and shape of network architecture is tightly related to the individuals' attributes. The authors introduce a method to reconstruct nodes' intimacy degree based on local interaction. Then we provide an intimacy based approach for orientation in networks. The authors find that the basic reason of efficient search in social networks is that the degree of "intimacy" of each pair of nodes decays with the length of their shortest path exponentially. Meanwhile, the model can explain the hubs limitation which was observed in real-world experiment.