9 research outputs found
Research on Adaptive Neural Network Control System Based on Nonlinear U-Model with Time-Varying Delay
U-model can approximate a large class of smooth nonlinear time-varying delay system to any accuracy by using time-varying delay parameters polynomial. This paper proposes a new approach, namely, U-model approach, to solving the problems of analysis and synthesis for nonlinear systems. Based on the idea of discrete-time U-model with time-varying delay, the identification algorithm of adaptive neural network is given for the nonlinear model. Then, the controller is designed by using the Newton-Raphson formula and the stability analysis is given for the closed-loop nonlinear systems. Finally, illustrative examples are given to show the validity and applicability of the obtained results
Identifying Causal Structure in Dynamical Systems
We present a method for automatically identifying the causal structure of a
dynamical control system. Through a suitable experiment design and subsequent
causal analysis, the method reveals, which state and input variables of the
system have a causal influence on each other. The experiment design builds on
the concept of controllability, which provides a systematic way to compute
input trajectories that steer the system to specific regions in its state
space. For the causal analysis, we leverage powerful techniques from causal
inference and extend them to control systems. Further, we derive conditions
that guarantee discovery of the true causal structure of the system and show
that the obtained knowledge of the causal structure reduces the complexity of
model learning and yields improved generalization capabilities. Experiments on
a robot arm demonstrate reliable causal identification from real-world data and
extrapolation to regions outside the training domain
Topology Detection for Output-Coupling Weighted Complex Dynamical Networks with Coupling and Transmission Delays
Topology detection for output-coupling weighted complex dynamical networks with two types of time delays is investigated in this paper. Different from existing literatures, coupling delay and transmission delay are simultaneously taken into account in the output-coupling network. Based on the idea of the state observer, we build the drive-response system and apply LaSalle’s invariance principle to the error dynamical system of the drive-response system. Several convergent criteria are deduced in the form of algebraic inequalities. Some numerical simulations for the complex dynamical network, with node dynamics being chaotic, are given to verify the effectiveness of the proposed scheme
Modeling and analysis of the dynamic behavior of the XlnR regulon in Aspergillus niger
Background: In this paper the dynamics of the transcription-translation system for XlnR regulon in Aspergillus niger is modeled. The model is based on Hill regulation functions and uses ordinary differential equations. The network response to a trigger of D-xylose is considered and stability analysis is performed. The activating, repressive feedback, and the combined effect of the two feedbacks on the network behavior are analyzed. Results: Simulation and systems analysis showed significant influence of activating and repressing feedback on metabolite expression profiles. The dynamics of the D-xylose input function has an important effect on the profiles of the individual metabolite concentrations. Variation of the time delay in the feedback loop has no significant effect on the pattern of the response. The stability and existence of oscillatory behavior depends on which proteins are involved in the feedback loop. Conclusions: The dynamics in the regulation properties of the network are dictated mainly by the transcription and translation degradation rate parameters, and by the D-xylose consumption profile. This holds true with and without feedback in the network. Feedback was found to significantly influence the expression dynamics of genes and proteins. Feedback increases the metabolite abundance, changes the steady state values, alters the time trajectories and affects the response oscillatory behavior and stability conditions. The modeling approach provides insight into network behavioral dynamics particularly for small-sized networks. The analysis of the network dynamics has provided useful information for experimental design for future in vitro experimental wor
Pinning Control Strategy of Multicommunity Structure Networks
In order to investigate the effects of community structure on synchronization, a pinning control strategy is researched in a class of complex networks with community structure in this paper. A feedback control law is designed based on the network community structure information. The stability condition is given and proved by using Lyapunov stability theory. Our research shows that as to community structure networks, there being a threshold hT≈5, when coupling strength bellows this threshold, the stronger coupling strength corresponds to higher synchronizability; vice versa, the stronger coupling strength brings lower synchronizability. In addition the synchronizability of overlapping and nonoverlapping community structure networks was simulated and analyzed; while the nodes were controlled randomly and intensively, the results show that intensive control strategy is better than the random one. The network will reach synchronization easily when the node with largest betweenness was controlled. Furthermore, four difference networks’ synchronizability, such as Barabási-Albert network, Watts-Strogatz network, Erdös-Rényi network, and community structure network, are simulated; the research shows that the community structure network is more easily synchronized under the same control strength
Multilayer Networks
In most natural and engineered systems, a set of entities interact with each
other in complicated patterns that can encompass multiple types of
relationships, change in time, and include other types of complications. Such
systems include multiple subsystems and layers of connectivity, and it is
important to take such "multilayer" features into account to try to improve our
understanding of complex systems. Consequently, it is necessary to generalize
"traditional" network theory by developing (and validating) a framework and
associated tools to study multilayer systems in a comprehensive fashion. The
origins of such efforts date back several decades and arose in multiple
disciplines, and now the study of multilayer networks has become one of the
most important directions in network science. In this paper, we discuss the
history of multilayer networks (and related concepts) and review the exploding
body of work on such networks. To unify the disparate terminology in the large
body of recent work, we discuss a general framework for multilayer networks,
construct a dictionary of terminology to relate the numerous existing concepts
to each other, and provide a thorough discussion that compares, contrasts, and
translates between related notions such as multilayer networks, multiplex
networks, interdependent networks, networks of networks, and many others. We
also survey and discuss existing data sets that can be represented as
multilayer networks. We review attempts to generalize single-layer-network
diagnostics to multilayer networks. We also discuss the rapidly expanding
research on multilayer-network models and notions like community structure,
connected components, tensor decompositions, and various types of dynamical
processes on multilayer networks. We conclude with a summary and an outlook.Comment: Working paper; 59 pages, 8 figure
Structure identification of uncertain general complex dynamical networks with time delay
It is well known that many real-world complex networks have various uncertain information, such as unknown or uncertain topological structure and node dynamics. The structure identification problem has theoretical and practical importance for uncertain complex dynamical networks. At the same time, time delay often appears in the state variables or coupling coefficients of various practical complex networks. This paper initiates a novel approach for simultaneously identifying the topological structure and unknown parameters of uncertain general complex networks with time delay. In particular, this method is also effective for uncertain delayed complex dynamical networks with different node dynamics. Moreover, the proposed method can be easily extended to monitor the on-line evolution of network topological structure. Finally, three representative examples are then given to verify the effectiveness of the proposed approach
Structure identification of uncertain general complex dynamical networks with time delay
It is well known that many real-world complex networks have various uncertain information, such as unknown or uncertain topological structure and node dynamics. The structure identification problem has theoretical and practical importance for uncertain complex dynamical networks. At the same time, time delay often appears in the state variables or coupling coefficients of various practical complex networks. This paper initiates a novel approach for simultaneously identifying the topological structure and unknown parameters of uncertain general complex networks with time delay. In particular, this method is also effective for uncertain delayed complex dynamical networks with different node dynamics. Moreover, the proposed method can be easily extended to monitor the on-line evolution of network topological structure. Finally, three representative examples are then given to verify the effectiveness of the proposed approach