Adaptive Dynamic Network Research Articles

Emergent Criticality through Adaptive Information Processing in Boolean Networks

We study information processing in populations of boolean networks with evolving connectivity and systematically explore the interplay between the learning capability, robustness, the network topology, and the task complexity. We solve a long-standing open question and find computationally that, for large system sizes N, adaptive information processing drives the networks to a critical connectivity K(c)=2. For finite size networks, the connectivity approaches the critical value with a power law of the system size N. We show that network learning and generalization are optimized near criticality, given that the task complexity and the amount of information provided surpass threshold values. Both random and evolved networks exhibit maximal topological diversity near K(c). We hypothesize that this diversity supports efficient exploration and robustness of solutions. Also reflected in our observation is that the variance of the fitness values is maximal in critical network populations. Finally, we discuss implications of our results for determining the optimal topology of adaptive dynamical networks that solve computational tasks.

Comment on “Cluster synchronization in the adaptive complex dynamical networks via a novel approach” [Phys. Lett. A 375 (2011) 1559

In the Letter “Cluster synchronization in the adaptive complex dynamical networks via a novel approach” [Phys. Lett. A 375 (2011) 1559], there are several errors in the proof of theorems.

Cluster synchronization in the adaptive complex dynamical networks via a novel approach

This Letter investigates cluster synchronization in the adaptive complex dynamical networks with nonidentical nodes by a local control method and a novel adaptive strategy for the coupling strengths of the networks. In this approach, the coupling strength of each node adjusts adaptively only based on the state information of its neighborhood. By means of the proposed scheme, the sufficient conditions for achieving cluster synchronization are derived analytically by utilizing Lyapunov stability theory. It is demonstrated that the synchronization performance is sensitively affected by the control gain, the inner-coupling matrix and the network topological structure. The numerical simulations are performed to verify the effectiveness of the theoretical results.

Chaos Synchronization Using Adaptive Dynamic Neural NetworkController with Variable Learning Rates

This paper addresses the synchronization of chaotic gyros with unknown parameters and external disturbance via an adaptive dynamic neural network control (ADNNC) system. The proposed ADNNC system is composed of a neural controller and a smooth compensator. The neural controller uses a dynamic RBF (DRBF) network to online approximate an ideal controller. The DRBF network can create new hidden neurons online if the input data falls outside the hidden layer and prune the insignificant hidden neurons online if the hidden neuron is inappropriate. The smooth compensator is designed to compensate for the approximation error between the neural controller and the ideal controller. Moreover, the variable learning rates of the parameter adaptation laws are derived based on a discrete‐type Lyapunov function to speed up the convergence rate of the tracking error. Finally, the simulation results which verified the chaotic behavior of two nonlinear identical chaotic gyros can be synchronized using the proposed ADNNC scheme.

Adaptive dynamic friction observer and recurrent fuzzy neural network estimator design with backstepping control

In this study, robust non-linear dynamic friction control is considered using a dynamic friction observer and intelligent control. An adaptive dynamic friction observer based on the LuGre friction model is proposed to estimate the friction parameters and a directly immeasurable friction state variable. A recurrent fuzzy neural network (RFNN) approximator and reconstructed error compensator are also designed to give additional robustness to the control system under friction model uncertainty. A proposed composite control scheme with a basic backstepping controller is applied to the position tracking control of the servo system. The performances of the proposed friction observer and the friction controller are demonstrated by some simulations and experiments.

Adaptive dynamic probabilistic networks for distributed uncertainty processing

Uncertainty processing is a core task in many applications of distributed systems. Typical distributed systems have local processing nodes to collect information, which usually contain uncertainties, and do computational work. The nodes can interact with each other, and they evolve with time. A promising way of modelling and processing uncertainties in these systems is to use graphical models to form beliefs about the required information. Dynamic probabilistic networks for distributed uncertainty processing are presented in this paper. Two approaches are given, and comparison shows that the model with the more state-of-art approach performs better. Since it is not possible to obtain enough knowledge to construct an exact model at the beginning, the model needs to adjust itself when evolving. Therefore we have developed a parameter update algorithm to make the model adapt to the changing environment. Experiments are presented to show the effectiveness of the models and the algorithms.

Adaptive dynamical networks via neighborhood information: Synchronization and pinning control

In this paper, we introduce a model of an adaptive dynamical network by integrating the complex network model and adaptive technique. In this model, the adaptive updating laws for each vertex in the network depend only on the state information of its neighborhood, besides itself and external controllers. This suggests that an adaptive technique be added to a complex network without breaking its intrinsic existing network topology. The core of adaptive dynamical networks is to design suitable adaptive updating laws to attain certain aims. Here, we propose two series of adaptive laws to synchronize and pin a complex network, respectively. Based on the Lyapunov function method, we can prove that under several mild conditions, with the adaptive technique, a connected network topology is sufficient to synchronize or stabilize any chaotic dynamics of the uncoupled system. This implies that these adaptive updating laws actually enhance synchronizability and stabilizability, respectively. We find out that even though these adaptive methods can succeed for all networks with connectivity, the underlying network topology can affect the convergent rate and the terminal average coupling and pinning strength. In addition, this influence can be measured by the smallest nonzero eigenvalue of the corresponding Laplacian. Moreover, we provide a detailed study of the influence of the prior parameters in this adaptive laws and present several numerical examples to verify our theoretical results and further discussion.

Adaptive dynamic networks as models for the immune system and autocatalytic sets.

Annals of the New York Academy of SciencesVolume 504, Issue 1 p. 118-131 Adaptive Dynamic Networks as Models for the Immune System and Autocatalytic Sets J. DOYNE FARMER, J. DOYNE FARMER Theoretical Division Los Alamos, National Laboratory Los Alamos, New Mexico 87545Search for more papers by this authorSTUART A. KAUFFMAN, STUART A. KAUFFMAN Department of Biochemistry and Biophysics, School of Medicine University of Pennsylvania Philadelphia, Pennsylvania 19104Search for more papers by this authorNORMAN H. PACKARD, NORMAN H. PACKARD The Center for Nonlinear Studies Los Alamos, National Laboratory Los Alamos, New Mexico 87545 Permanent address: Physics Dept. and Center for Complex Systems Research, University of Illinois, Urbana, IL 61801.Search for more papers by this authorALAN S. PERELSON, ALAN S. PERELSON Theoretical Division Los Alamos, National Laboratory Los Alamos, New Mexico 87545Search for more papers by this author J. DOYNE FARMER, J. DOYNE FARMER Theoretical Division Los Alamos, National Laboratory Los Alamos, New Mexico 87545Search for more papers by this authorSTUART A. KAUFFMAN, STUART A. KAUFFMAN Department of Biochemistry and Biophysics, School of Medicine University of Pennsylvania Philadelphia, Pennsylvania 19104Search for more papers by this authorNORMAN H. PACKARD, NORMAN H. PACKARD The Center for Nonlinear Studies Los Alamos, National Laboratory Los Alamos, New Mexico 87545 Permanent address: Physics Dept. and Center for Complex Systems Research, University of Illinois, Urbana, IL 61801.Search for more papers by this authorALAN S. PERELSON, ALAN S. PERELSON Theoretical Division Los Alamos, National Laboratory Los Alamos, New Mexico 87545Search for more papers by this author First published: July 1987 https://doi.org/10.1111/j.1749-6632.1987.tb48728.xCitations: 33 AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Citing Literature Volume504, Issue1Perspectives in Biological Dynamics and Theoretical MedicineJuly 1987Pages 118-131 RelatedInformation