Journal of Systems Science & Complexity 

About: Journal of Systems Science & Complexity is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Computer science & Nonlinear system. It has an ISSN identifier of 1009-6124. Over the lifetime, 1616 publications have been published receiving 14012 citations. The journal is also known as: JSSC & Journal of systems science & complexity.

Studying Complex Adaptive Systems

Abstract: Complex adaptive systems (cas) – systems that involve many components that adapt or learn as they interact – are at the heart of important contemporary problems. The study of cas poses unique challenges: Some of our most powerful mathematical tools, particularly methods involving fixed points, attractors, and the like, are of limited help in understanding the development of cas. This paper suggests ways to modify research methods and tools, with an emphasis on the role of computer-based models, to increase our understanding of cas.

Consensus control for leader-following multi-agent systems with measurement noises

Abstract: This work is concerned with consensus control for a class of leader-following multi-agent systems (MASs). The information that each agent received is corrupted by measurement noises. To reduce the impact of noises on consensus, time-varying consensus gains are adopted, based on which consensus protocols are designed. By using the tools of stochastic analysis and algebraic graph theory, a sufficient condition is obtained for the protocol to ensure strong mean square consensus under the fixed topologies. This condition is shown to be necessary and sufficient in the noise-free case. Furthermore, by using a common Lyapunov function, the result is extended to the switching topology case.

Crude oil price forecasting with tei@i methodology ∗

Abstract: The difficulty in crude oil price forecasting, due to inherent complexity, has attracted much attention of academic researchers and business practitioners. Various meth- ods have been tried to solve the problem of forecasting crude oil prices. However, all of the existing models of prediction can not meet practical needs. Very recently, Wang and Yu proposed a new methodology for handling complex systems—TEI@I methodology by means of a systematic integration of text mining, econometrics and intelligent techniques. Within the framework of TEI@I methodology, econometrical models are used to model the linear components of crude oil price time series (i.e., main trends) while nonlinear compo- nents of crude oil price time series (i.e., error terms) are modelled by using artificial neural network (ANN) models. In addition, the impact of irregular and infrequent future events on crude oil price is explored using web-based text mining (WTM) and rule-based expert systems (RES) techniques. Thus, a fully novel nonlinear integrated forecasting approach with error correction and judgmental adjustment is formulated to improve prediction per- formance within the framework of the TEI@I methodology. The proposed methodology and the novel forecasting approach are illustrated via an example.

Control and stabilization of the Korteweg-de Vries equation: recent progresses

Abstract: The study of the control and stabilization of the KdV equation began with the work of Russell and Zhang in late 1980s. Both exact control and stabilization problems have been intensively studied since then and significant progresses have been made due to many people's hard work and contributions. In this article, the authors intend to give an overall review of the results obtained so far in the study but with an emphasis on its recent progresses. A list of open problems is also provided for further investigation.

Soft Control on Collective Behavior of a Group of Autonomous Agents By a Shill Agent

Abstract: This paper asks a new question: how can we control the collective behavior of self-organized multi-agent systems? We try to answer the question by proposing a new notion called ‘Soft Control’, which keeps the local rule of the existing agents in the system. We show the feasibility of soft control by a case study. Consider the simple but typical distributed multi-agent model proposed by Vicsek et al. for flocking of birds: each agent moves with the same speed but with different headings which are updated using a local rule based on the average of its own heading and the headings of its neighbors. Most studies of this model are about the self-organized collective behavior, such as synchronization of headings. We want to intervene in the collective behavior (headings) of the group by soft control. A specified method is to add a special agent, called a ‘Shill’, which can be controlled by us but is treated as an ordinary agent by other agents. We construct a control law for the shill so that it can synchronize the whole group to an objective heading. This control law is proved to be effective analytically and numerically. Note that soft control} is different from the approach of distributed control}. It is a natural way to intervene in the distributed systems. It may bring out many interesting issues and challenges on the control of complex systems.