14313020
Global Exponential Synchronization for a Class of Delayed Chaotic Networks Using Partial System States
This Letter deals with the global exponential synchronization of a class of chaotic networks with time delays. Based on the Halanay inequality technique and the Lyapunov stability theory, the global synchronization of a network about its all variables is derived by only considering the global synchronization of its partial variables, and a delay-independent criteria and decentralized control law is derived to ensure the adaptive exponential synchronization of the model and these results are proved to be able to guarantee different exponential convergence rates for the controlled states and the uncontrolled states of the error systems, respectively, and the simpler, less conservative and more efficient results are easy to be verified in engineering applications. Finally, an illustrative example is presented to verify the effectiveness of the presented synchronization scheme.
21487383
Coordinated Collective Motion of Groups of Autonomous Mobile Robots with Directed Interconnected Topology
Coordinated collective motion of groups of autonomous mobile robots is studied. A qualitative analysis for the collective dynamics of multiple autonomous robots with directed interconnected topology using nearest neighbor rules is given. A necessary and sufficient graphical condition is proposed to guarantee that the headings of all robots converge to the same heading. The graph having a globally reachable node plays a crucial role in convergence analysis. Furthermore, we show that the globally reachable node having no neighbors serves as a group leader as a special case
10763979
Flocking Motion of Mobile Agents with a Leader Based on Distance-Dependent Adjacency Matrix
In this study, a novel procedure is presented for control and analysis of multiple autonomous agents with point mass dynamics achieving flocking motion with a time variant leader. Two control laws are proposed for a group of autonomous agents to achieve flocking formations related to two different centers (mass center and geometric center) of the flock. The first one is designed for flocking motion based on mass center and the second for geometric center. Distance-dependent adjacency matrix is used to quantify the way agents influence each other. Theoretical analysis is established to indicate the stability (cohesiveness, collision avoidance and velocity matching) of the control systems using classical Lyapunov theory. Finally, a simulation example is demonstrated to validate the theoretical results.
31796281
Reliable Robust Controller Design for Nonlinear State-Delayed Systems Based on Neural Networks
An approach is investigated for the adaptive guaranteed cost control design for a class of nonlinear state-delayed systems. The nonlinear term is approximated by a linearly parameterized neural networks( LPNN). A linear state feedback H∞ control law is presented. An adaptive weight adjustment mechanism for the neural networks is developed to ensure H∞ regulation performance. It is shown that the control gain matrices and be transformed into a standard linear matrix inequality problem and solved via a developed recurrent neural network.
3115667
Synchronization of Rikitake Chaotic Attractor via Partial System States
This paper treats the chaotic synchronization problem for Rikitake chaotic systems via unidirectional coupling with partial systems states. Two control approaches, namely the self coupling of single state variable and nonlinear feedback controller with partial systems states, are investigated. Based on the Lyapunov stability technique, control laws based on single variable of driven system are derived such that the two identical Rikitake chaotic systems are asymptotically synchronized, where the control scheme doesn't need additional energy and it just relies on the self coupling of single state variable of the chaotic systems. Meanwhile, the complete synchronization for two identical Rikitake chaotic systems by feedback control laws with partial systems states is also derived using the conditions of asymptotical stability and exponential stability. Finally, numerical experimental results of two synchronized Rikitake chaotic systems are presented.
18521632
Mining Most General Multidimensional Summarization of Probably Groups in Data Warehouses
Data summarization is an important data analysis task in data warehousing and online analytic processing. In this paper, we consider a novel type of summarization queries, probable group queries , uch as “What are the groups of patients that have a50% or more opportunity to get lung cancer than the average?” An aggregate cell satisfying the requirement is called aprobable group . To make the answer succinct and effective, we propose that only the most general probable groups should be mined. For example, if both groups (smoking, drinking) and (smoking, *) are probable, then the former groups should not be returned. The problem of mining the most general probable groups is challenging since the probable groups can be widely scattered in the cube lattice, and do not present any monotonicity in group containment order. We extend the state-of-the-art BUC algorithm to tackle the problem, and develop techniques and heuristics to speed up the search. An extensive performance study is reported to illustrate the effect of our approach.
17495157
Summarization for Internet News Based on Clustering Algorithm
For the explosion of information in the World Wide Web, this paper proposed a new method of summarization for Internet news based on clustering algorithm. It used google search engine to extract relevant documents, and mixed query sentence into sentences set which segmented from relevant document set, then this paper adopted effective cluster algorithm to cluster all the sentences. In the period of summary generation, the summary sentences were extracted by turns from the clusters which query sentence in. Experimental result shows that the proposed summarization method can improve the performance of summary, clustering algorithm is effective.
16267058
Petroleum-contaminated groundwater remediation systems design: A data envelopment analysis based approach
Groundwater contamination is one of important environmental problems at petroleum-related sites, which is causing more and more attention. It can bring serious adverse effects on the environment and human health. Design of a groundwater remediation system is desired for supporting the management of petroleum-contaminated sites. Due to high remediation cost and extensive alternatives, efficient remediation design needs to be determined. Data envelopment analysis (DEA) is an approach to measure the relative efficiencies of decision-making units (DMUs) with multiple inputs and multiple outputs without underlying assumptions. However, few of these studies were applied to the design of groundwater remediation systems, where efficient alternatives for remediation systems design need to be determined. This paper proposes a hybrid decision support approach based on data envelopment analysis to determine the most preferable design alternatives for groundwater remediation systems. The total remediation system cost, amount of additional wells, and manpower requirements will be employed as the input variables, while the removal efficiency and technical feasibility as the output variables. The proposed approach will be applied to a case study of designing a groundwater remediation system at a petroleum-contaminated site in western Canada. The most preferable design alternative can be obtained to provide effective decision support for cleaning up the contaminated site.
13982371
Parallel implementation of the global optimization algorithm based on uniform distributional design and Powell method
The global optimization for complex functions method is in dire need of new computation. By combining the Powell method, which finds a partial optimum solution, with the uniform distribution design method, which uses evenly spaced test points at higher dimensional space, allows for a new algorithm which is capable of solving the global optimization function. This new algorithm is robust enough to search global optimal solution. Its time complexity is proportional to the number of initial points and to the square dimensions of the problem, and its spatial complexity is in proportion to the number of the initial points and the dimensions of the problem. Also, the algorithm has good parallel efficiency and will have broader potential applications
16741593
A Log Analysis Audit Model Based on Optimized Clustering Algorithm
In view of the problem how to detect the network unknown attacks, a security log analysis audit model based on optimized clustering algorithm is proposed in this paper. Since the main question which influence the clustering algorithm application in the log analysis is uneasy to determine the network attack type and the cluster number, so we bring forward an optimized cluster algorithm to solve this problem. By means of simulated experiments, this algorithm is proved feasible, efficient and extensible for unknown intrusion detection.