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This paper describes a proportional-differential (PD) control algorithm as a new active queue management (AQM) scheme for TCP/IP congestion control. From the viewpoint of the control theory, TCP congestion control system can be regarded as a feedback regulating system. In this paper, a robust AQM called PD-controller is proposed. The design principles of PD-controller are presented in details. Its performance is extensively evaluated by simulations. The results demonstrate that the PD-controller AQM is stable and robust against traffic load fluctuations, UDP and HTTP disturbances. Its superiority over other AQMs is also demonstrated.

In this article, we propose and study an extension of the Chen–Lai algorithm for chaotification of discrete-time dynamical systems. The proposed method is a simple but mathematically rigorous feedback control design method that can gradually make all the Lyapunov exponents of the controlled system strictly positive for any given n-dimensional dynamical system that has a uniformly bounded Jacobian but otherwise could be originally nonchaotic or even asymptotically stable.

The nodes in a community within a network are much more connected to each other than to the others outside the community in the same network. This phenomenon has been commonly observed from many real-world networks, ranging from social to biological even to technical networks. Meanwhile, the number of communities in some real-world networks, such as the Internet and most social networks, are evolving with time. To model this kind of networks, the present Letter proposes a multi-local-world (MLW) model to capture and describe their essential topological properties. Based on the mean-field theory, the degree distribution of this model is obtained analytically, showing that the generated network has a novel topological feature as being not completely random nor completely scale-free but behaving somewhere between them. As a typical application, the MLW model is applied to characterize the Internet against some other models such as the BA, GBA, Fitness and HOT models, demonstrating the superiority of the new model.

In this paper, we investigate a networked prisoner's dilemma game where individuals' strategy-selection time scale evolves based on their historical learning information. We show that the more times the current strategy of an individual is learnt by his neighbors, the longer time he will stick on the successful behavior by adaptively adjusting the lifetime of the adopted strategy. Through characterizing the extent of success of the individuals with normalized payoffs, we show that properly using the learned information can form a positive feedback mechanism between cooperative behavior and its lifetime, which can boost cooperation on square lattices and scale-free networks.

A problem of stabilizing stochastically forced equilibria in nonlinear dynamic systems with incomplete information is studied. A new control approach based on the idea of synthesizing a desired stochastic sensitivity for an equilibrium is developed. The focus is on the case when the system states are observed only partially, with noisy observations. For the task of control, a dynamic regulator composed by feedback and filter is used. The problem of synthesizing the assigned stochastic sensitivity by this dynamic regulator is then considered. For a general nonlinear system, an algebraic equation connecting the stochastic sensitivity matrix with parameters of the regulator is derived. For the important case of two-dimensional stochastic nonlinear oscillators, explicit formulas of the dependence of the stochastic sensitivity on the regulator parameters are obtained. These theoretical results are constructively applied to the stabilization of the randomly forced equilibrium of the van der Pol oscillator.

In this paper, the problem of making a stable map chaotic by using smooth small-amplitude high-frequency feedback control is studied. The controlled map is mathematically proven to be chaotic in the sense of Li and Yorke. To demonstrate the practical usefulness of the proposed method, it is applied to a feedback boost switching regulator model operating in discontinuous mode. Copyright © 2000 John Wiley & Sons, Ltd.

Under three necessary conditions for preserving the essential qualitative properties of the 3D Lorenz system, a general 2D quadratic autonomous system is converted to a 2D Lorenz-type system (2DLTS). A canonical form of the 2DLTS is derived with aid of a normalization technique. It is found that the 2DLTS can be converted to the 2D Duffing oscillator model under certain conditions. Furthermore, it is shown that the 2DLTS undergoes pitchfork bifurcation and Hopf bifurcation. Finally, approximate periodic solutions of both the 2DLTS near the Hopf bifurcation point and a time-periodically forced system are obtained.

This paper proposes a novel stream encryption scheme with avalanche effect (SESAE). Using this scheme and an ideal pseudorandom number generator (PRNG) to generate d-bit segment binary key streams, one can encrypt a plaintext such that by using any key stream generated from a different seed to decrypt the ciphertext, the decrypted plaintext will become an avalanche-like text which has 2 d − 1 consecutive one’s with a high probability. As a cost, the required bits of the ciphertext are d times those of the plaintext. A corresponding avalanche-type encryption theorem is established. Two chaotic 12-bit segment PRNGs are designed. A generalized FIPS140 test and SESAE test for the two chaotic PRNGs, RC4 12-bit segment PRNG and 12-bit segment Matlab PRNG are implemented. The SESAE tests for 16-bit segment PRNGs are also compared. The results suggest that those PRNGs are able to generate the SESAEs which are similar to those generated via ideal PRNGs.

To investigate how consensus is reached on a large self-organized peer-to-peer network, we extended the naming game model commonly used in language and communication to Naming Game in Groups (NGG). Differing from other existing naming game models, in NGG everyone in the population (network) can be both speaker and hearer simultaneously, which resembles in a closer manner to real-life scenarios. Moreover, NGG allows the transmission (communication) of multiple words (opinions) for multiple intra-group consensuses. The communications among indirectly-connected nodes are also enabled in NGG. We simulated and analyzed the consensus process in some typical network topologies, including random-graph networks, small-world networks and scale-free networks, to better understand how global convergence (consensus) could be reached on one common word. The results are interpreted on group negotiation of a peer-to-peer network, which shows that global consensus in the population can be reached more rapidly when more opinions are permitted within each group or when the negotiating groups in the population are larger in size. The novel features and properties introduced by our model have demonstrated its applicability in better investigating general consensus problems on peer-to-peer networks.

In this paper, we present an approach for neural networks (NN) based identification of unknown nonlinear dynamical systems undergoing periodic or periodic-like (recurrent) motions. Among various types of NN architectures, we use a dynamical version of the localized RBF neural network, which is shown to be particularly suitable for identification in a dynamical framework. With the associated properties of localized RBF networks, especially the one concerning the persistent excitation (PE) condition for periodic trajectories, the proposed approach achieves sufficiently accurate identification of system dynamics in a local region along the experienced system trajectory. In particular, for neurons whose centers are close to the trajectories, the neural weights converge to a small neighborhood of a set of optimal values; while for other neurons with centers far away from the trajectories, the neural weights are not updated and are almost unchanged. The proposed approach implements a sort of "deterministic learning" in the sense that deterministic features of nonlinear dynamical systems are learned not by algorithms from statistical principles, but in a dynamical, deterministic manner, utilizing results from adaptive systems theory. The nature of this deterministic learning is closely related to the exponentially stability of a class of nonlinear adaptive systems. Simulation studies are included to demonstrate the effectiveness of the proposed approach.