Publications

Constructing a one-way hash function based on the unified chaotic system

A new one-way hash function based on the unified chaotic system is constructed. With different values of a key parameter, the unified chaotic system represents different chaotic systems, based on which the one-way hash function algorithm is constructed with three round operations and an initial vector on an input message. In each round operation, the parameters are processed by three different chaotic systems generated from the unified chaotic system. Feed-forwards are used at the end of each round operation and at the end of each element of the message processing. Meanwhile, in each round operation, parameter-exchanging operations are implemented. Then, the hash value of length 160 bits is obtained from the last six parameters. Simulation and analysis both demonstrate that the algorithm has great flexibility, satisfactory hash performance, weak collision property, and high security.

Decoupling of Filtering Equations

No abstract is provided for this article.

Small groups nurturing collective wisdom: The punishment-prediction reinforcement learning mechanism for multi-group cooperation

Modified extended Kalman filtering for supervised learning

We apply the modified extended Kalman filter to develop a learning algorithm for feedforward networks. The resulting algorithm improves the existing extended Kalman filter based scheme in both speed of convergence and accuracy of estimation.

Cryptanalysis of a chaotic block cipher with external key and its improved version

Recently, Pareek et al. proposed a symmetric key block cipher using multiple one-dimensional chaotic maps. This paper reports some new findings on the security problems of this kind of chaotic cipher: (1) a number of weak keys exist; (2) some important intermediate data of the cipher are not sufficiently random; (3) the whole secret key can be broken by a known-plaintext attack with only 120 consecutive known plain-bytes in one known plaintext. In addition, it is pointed out that an improved version of the chaotic cipher proposed by Wei et al. still suffers from all the same security defects.

Global Robust Stability and Synchronization of Networks With Lorenz-Type Nodes

This brief addresses in a unified framework both global robust stability and synchronization problems for a class of directed networks with Lorenz-type nodes. When the key parameter in the node equation is modified, it covers the Lorenz, Chen, and Lu types of networks as special cases. Based on an observation about some special nonlinear characteristics of Lorenz-type systems, simple conditions are derived for global stability and synchronization of such networks, where the typical Lipschitz-type condition for nonlinear functions is not needed. By combining the pinning control strategy and a new linear control law, synchronization of a network with different nodes can be achieved. Several examples are given for illustration.

Finite-Time Synchronization of Chaotic Complex Networks with

Abstract: This paper is concerned with the problem of finite-time synchronization incomplex networks with stochastic noise perturbations. By using a novel finite-timeL-operator differential inequality and other inequality techniques, some novel sufficientconditions are obtained to ensure finite-time stochastic synchronization for the complexnetworks concerned, where the coupling matrix need not be symmetric. The effectsof control parameters on synchronization speed and time are also analyzed, and thesynchronization time in this paper is shorter than that in the existing literature. The resultshere are also applicable to both directed and undirected weighted networks without anyinformation of the coupling matrix. Finally, an example with numerical simulationsis givento demonstrate the effectiveness of the proposed method.Keywords: chaotic complex networks; finite-time synchronization; stochasticsynchronization; L-operator differential inequality; stochastic disturbance1. IntroductionIn recent years, complex networks have been shown to exist in many different areas in the realword [1], such as Internet networks, the Word Wide Web, food chains, relationship networks, andso on. A complex network is composed of a set of interconnected nodes, where the nodes andconnections can represent anything. According to different ways of connections and whether there are

Pontibacter korlensis sp. nov., isolated from the desert of Xinjiang, China

Two Gram-negative, rod-shaped, gliding and pink-pigmented bacterial strains, X14-1T and X19-1, were isolated from a mixture of sand samples collected from the desert of Xinjiang, China, and characterized by using a polyphasic taxonomic approach. Strains X14-1T and X19-1 contained MK-7 as the predominant menaquinone. The major cellular fatty acids included iso-C15 : 0, iso-C17 : 0 3-OH, summed feature 3 and summed feature 4. The DNA G+C contents of strains X14-1T and X19-1 were 48.2 and 48.9 mol%, respectively. 16S rRNA gene sequence analysis showed that the isolates were highly related to each other (99.2 %) and confirmed their placement in the genus Pontibacter. Strains X14-1T and X19-1 exhibited 16S rRNA gene similarity levels of 95.0-97.2 % to the type strains of the two Pontibacter species with validly published names. DNA-DNA hybridization experiments revealed a high level of relatedness between the two new isolates (82 %), but low levels of relatedness between strain X14-1T and the phylogenetically most closely related species Pontibacter actiniarum KMM 6156T (51 %). On the basis of genotypic and phenotypic evidence, strains X14-1T and X19-1 are considered to represent a novel species of the genus Pontibacter, for which the name Pontibacter korlensis sp. nov. is proposed. The type strain is X14-1T (=CCTCC AB 206081T=NRRL B-51097T).

REALIZATION AND BIFURCATION OF BOOLEAN FUNCTIONS VIA CELLULAR NEURAL NETWORKS

In this work, we study the realization and bifurcation of Boolean functions of four variables via a Cellular Neural Network (CNN). We characterize the basic relations between the genes and the offsets of an uncoupled CNN as well as the basis of the binary input vectors set. Based on the analysis, we have rigorously proved that there are exactly 1882 linearly separable Boolean functions of four variables, and found an effective method for realizing all linearly separable Boolean functions via an uncoupled CNN. Consequently, any kind of linearly separable Boolean function can be implemented by an uncoupled CNN, and all CNN genes that are associated with these Boolean functions, called the CNN gene bank of four variables, can be easily determined. Through this work, we will show that the standard CNN invented by Chua and Yang in 1988 indeed is very essential not only in terms of engineering applications but also in the sense of fundamental mathematics.

Unstable Limit Cycles and Singular Attractors in a Two-Dimensional Memristor-Based Dynamic System

This paper reports the finding of unstable limit cycles and singular attractors in a two-dimensional dynamical system consisting of an inductor and a bistable bi-local active memristor. Inspired by the idea of nested intervals theorem, a new programmable scheme for finding unstable limit cycles is proposed, and its feasibility is verified by numerical simulations. The unstable limit cycles and their evolution laws in the memristor-based dynamic system are found from two subcritical Hopf bifurcation domains, which are subdomains of twin local activity domains of the memristor. Coexisting singular attractors are discovered in the twin local activity domains, apart from the two corresponding subcritical Hopf bifurcation domains. Of particular interest is the coexistence of a singular attractor and a period-2 or period-3 attractor, observed in numerical simulations.

A direct solution to the stochastic inverse eigenvalue problem for complex-valued eigenspectra

We present a direct solution to the problem of constructing a stochastic matrix with prescribed eigenspectrum, widely referred to as the stochastic inverse eigenvalue problem. The solution uses Markov state disaggregation to construct a Markov chain with the associated stochastic transition matrix possessing the required eigenspectrum. Existing solutions that follow the same approach are limited to constructing matrices with real-valued eigenspectra. The novel solution directly constructs matrices with complex-valued eigenspectra by applying a new disaggregation technique in tandem with a technique from a previous solution. Due to this generalization, the novel solution is able to successfully model physical systems from a larger family. Furthermore, the novel solution constructs the matrix in a finite and predetermined number of iterations, and without numerical approximation. The solution is demonstrated by deriving an expression for a set of 4 × 4 stochastic matrices sharing the same prescribed complex-valued eigenspectrum and indexed by a real parameter.

Toward Zero-Determinant Strategies for Optimal Decision Making in Crowdsourcing Systems

The crowdsourcing system is an internet-based distributed problem-solving and production organization model, which has been applied in human–computer interaction, databases, natural language processing, machine learning and other fields. It guides the public to complete some tasks through specific strategies and methods. However, rational and selfish workers in crowdsourcing systems will submit solutions of different qualities in order to maximize their own benefits. Therefore, how to choose optimal strategies for selfish workers to maximize their benefits is important and crucial in such a scenario. In this paper, we propose a decision optimization method with incomplete information in a crowdsourcing system based on zero-determinant (ZD) strategies to help workers make optimal decisions. We first formulate the crowdsourcing problem, where workers have “winner-takes-all” rules as an iterated game with incomplete information. Subsequently, we analyze the optimal decision of workers in crowdsourcing systems in terms of ZD strategies, for which we find conditions to reach the maximum payoff of a focused worker. In addition, the analysis helps understand what solutions selfish workers will submit under the condition of having incomplete information. Finally, numerical simulations illustrate the performances of different strategies and the effects of the parameters on the payoffs of the focused worker.

Generalized Lorenz Systems Family

This article briefly introduces the generalized Lorenz systems family, which includes the classical Lorenz system and the relatively new Chen system as special cases, with infinitely many related but not topologically equivalent chaotic systems in between.

Fuzzy PID control of a flexible-joint robot arm with uncertainties from time-varying loads

This paper presents the design and experiment of a fuzzy proportional integral derivative (PID) controller for a flexible-joint robot arm with uncertainties from time-varying loads. Experimental results have shown remarkable tracking performance of this fuzzy PID controller, and have convincingly demonstrated that fuzzy logic control can be used for flexible-joint robot arms with uncertainties and it is quite robust. In this paper, the fuzzy PID controller is first described briefly, using a simple and practical PD+I controller configuration. This configuration preserves the linear structure of the conventional PD+I controller, but has nonconstant gains: the proportional, integral, and derivative gains are nonlinear functions of their input signals, which have self-tuning (adaptive) capabilities in set-point tracking performance. Moreover, these variable gains make the fuzzy PID controller robust with faster response time and less overshoot than its conventional counterpart. The proposed design was tested using a flexible-joint robot arm driven by a DC motor in a laboratory, where the arm was experienced with time-varying loads. Control performance by the conventional and fuzzy PID controllers for such a laboratory robotic system are both included in this paper for comparison.

Phase transition and hysteresis loop in structured games with global updating

We present a global payoff-based strategy updating model for studying cooperative behavior of a networked population. We adopt the Prisoner's Dilemma game and the snowdrift game as paradigms for characterizing the interactions among individuals. We investigate the model on regular, small-world, and scale-free networks, and find multistable cooperation states depending on the initial cooperator density. In particular for the snowdrift game on small-world and scale-free networks, there exist a discontinuous phase transition and hysteresis loops of cooperator density. We explain the observed properties by theoretical predictions and simulation results of the average number of neighbors of cooperators and defectors, respectively. Our work indicates that individuals with more neighbors have a trend to preserve their initial strategies, which has strong impacts on the strategy updating of individuals with fewer neighbors; while the fact that individuals with few neighbors have to become cooperators to avoid gaining the lowest payoff plays significant roles in maintaining and spreading of cooperation strategy.

IEEE Transactions on Cybernetics

No abstract is provided for this article.

Hidden and transient chaotic attractors in the attitude system of quadrotor unmanned aerial vehicle

Currently, research of quadrotor unmanned aerial vehicles (QUAV) focuses on the design of the controller and optimization of the control algorithm. Dynamical analysis of the attitude system of QUAV has also been studied. In this paper, the stability of equilibrium points is further analyzed based on their distribution and bifurcation. Multi-initial phase-portraits demonstrate the multistability and the dynamical process from sinks to chaos of the system. The axis of its yaw angular velocity is proven to be a stable manifold, but a little perturbation to it leads to chaotic motion of the QUAV subject to an appropriate parameter configuration. Multi-initial phase-portraits, multi-initial bifurcation diagrams and basins of attraction altogether confirm that the discovered chaotic attractors are hidden and transient. The transient characteristic of these hidden attractors is investigated, revealing that they are natural sinks, which is confirmed by very long-time simulations.

Karhunen–Loève decomposition approach to analyzing complex network synchronization

In this paper, the approach of the Karhunen–Loève decomposition, known also as the proper orthogonal modes (POMs), is taken to analyze phase synchronization of various complex networks with different topologies, namely the classic Kuramoto model, coupled chaotic maps with Gaussian delays and a chain of diffusively coupled bistable oscillators. In the case of the Kuramoto model, the POMs reveal the tendency and the level of synchronization with the increase of the coupling strength for globally coupled networks and scale-free networks, while periodic POMs are found in nearest-neighbor coupled networks. Furthermore, for cluster networks on the Kuramoto model, the first leading POMs based on different time intervals reveal that different sub-groups of nodes synchronize gradually to different levels, eventually leading to the complete phase synchronization. In the case of coupled chaotic maps, some properties of phase synchronization change with the coupling strength value. In the case of the chain of diffusively coupled bistable oscillators, several main POMs not only determine the network phase synchronization but also provide good reconstruction of the network responses.