2,312 publications from this institution
This paper introduces a new chaos generator, a switching piecewise-linear controller, which can create chaos from a three-dimensional linear system within a wide range of parameter values. Basic dynamical behaviors of the chaotic controlled system are investigated in some detail.
In recent years, various image encryption algorithms based on the permutation–diffusion architecture have been proposed where, however, permutation and diffusion are considered as two separate stages, both requiring image-scanning to obtain pixel values. If these two stages are combined, the duplicated scanning effort can be reduced and the encryption can be accelerated. In this paper, a fast image encryption algorithm with combined permutation and diffusion is proposed. First, the image is partitioned into blocks of pixels. Then, spatiotemporal chaos is employed to shuffle the blocks and, at the same time, to change the pixel values. Meanwhile, an efficient method for generating pseudorandom numbers from spatiotemporal chaos is suggested, which further increases the encryption speed. Theoretical analyses and computer simulations both confirm that the new algorithm has high security and is very fast for practical image encryption.
No abstract is provided for this article.
In this paper, a systematic design approach based on time-delay feedback is developed for anticontrol of chaos in a continuous-time system. This anticontrol method can drive a finite-dimensional, continuous-time, autonomous system from nonchaotic to chaotic, and can also enhance the existing chaos of an originally chaotic system. Asymptotic analysis is used to establish an approximate relationship between a time-delay differential equation and a discrete map. Anticontrol of chaos is then accomplished based on this relationship and the differential-geometry control theory. Several examples are given to verify the effectiveness of the methodology and to illustrate the systematic design procedure.
Bifurcation control generally means to design a controller that can modify the bifurcation characteristics of a bifurcating nonlinear system, thereby achieving some desirable dynamical behaviors. A typical objective is delay and/or stabilization of a given bifurcation. In this paper, we consider the problem of anti-control of bifurcations, that is, a certain bifurcation is created with desired location and properties by appropriate controls. In particular, washout filter-aided dynamic feedback control laws are given for the creation of Hopf bifurcations. As Hopf bifurcations give rise to limit cycles, anti-control of Hopf bifurcations can be viewed as one approach to design limit cycles and nonlinear oscillations into a system via feedback control when such dynamical behaviors are desirable.
In this paper, we investigate the almost sure stability of switched systems on randomly switching durations simultaneously with randomly switching interaction matrices. We not only allow the interaction matrix on each switching duration to take values randomly from either a countable, an uncountable, or even an unbounded state space, but also allow the corresponding probability density function to be time varying with the switching. We provide an example to show the difference between the almost sure stability and the moment stability. Then, we establish several practical stability criteria for switched systems, which may have linear or nonlinear subsystems. These stability criteria also enable us to find suitable conditions for realizing almost sure synchronization in complex networks, such as small-world networks, with both randomly switching durations and a few switching couplings that take values randomly in either an uncountable or an unbounded state space.
No abstract is provided for this article.
In this paper, some properties of a strictly <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper A"> <mml:semantics> <mml:mi>A</mml:mi> <mml:annotation encoding="application/x-tex">A</mml:annotation> </mml:semantics> </mml:math> </inline-formula>-coupled-expanding map in compact subsets of a metric space are studied, where <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper A"> <mml:semantics> <mml:mi>A</mml:mi> <mml:annotation encoding="application/x-tex">A</mml:annotation> </mml:semantics> </mml:math> </inline-formula> is a transition matrix. It is shown that this map has a compact invariant set on which it is topologically semi-conjugate to the subshift for <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper A"> <mml:semantics> <mml:mi>A</mml:mi> <mml:annotation encoding="application/x-tex">A</mml:annotation> </mml:semantics> </mml:math> </inline-formula>. If the subshift for <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper A"> <mml:semantics> <mml:mi>A</mml:mi> <mml:annotation encoding="application/x-tex">A</mml:annotation> </mml:semantics> </mml:math> </inline-formula> has positive topological entropy, then the map is chaotic in the sense of Li-Yorke. Moreover, in the one-dimensional case, the map is at most two-to-one conjugate to the subshift for <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper A"> <mml:semantics> <mml:mi>A</mml:mi> <mml:annotation encoding="application/x-tex">A</mml:annotation> </mml:semantics> </mml:math> </inline-formula> and chaotic in the sense of Devaney.
Generalized synchronization is a typical dynamical phenomenon in nonlinear systems, for which the real-valued setting has been widely investigated. The complex-valued functions relationship in generalized synchronization is equally important for complex-valued dynamical systems, which however are seldom studied. Complex parameters identification on the synchronization manifold remains an open problem owing to the absence of the persistent excitation (PE) condition in the complex field. This paper investigates generalized synchronization via a complex-valued vector mapping (CGS) for different-dimensional complex-variable chaotic (hyper-chaotic) systems (CVCSs) with complex parameters identification. Based on Lyapunov stability theory in the complex field and using an adaptive control method, some sufficient criteria are established to achieve CGS for CVCSs. Moreover, some necessary and sufficient criteria are derived to ensure complex parameters identification. Finally, the theoretical results are verified and demonstrated by reduced-order and increased-order simulation examples.
Recently, a new signal security system called TDCEA (two-dimensional circulation encryption algorithm) was proposed for real-time multimedia data transmission. This paper gives a comprehensive analysis on the security of TDCEA. The following security problems are found: (1) there exist some essential security defects in TDCEA; (2) two known-plaintext attacks can break TDCEA; (3) the chosen-plaintext and chosen-ciphertext versions of the aforementioned two known-plaintext attacks can break TDCEA even with a smaller complexity and a better performance. Some experiments are given to show the security defects of TDCEA and the feasibility of the proposed known-plaintext attacks. As a conclusion, TDCEA is not suitable for applications that require a high level of security.
Complex networks have attracted increasing attention from various fields of science and engineering today. Due to the finite speeds of transmission and spreading as well as traffic congestions, a signal or influence travelling through a complex network often is associated with time delays, and this is very common in biological and physical networks. In this paper, we introduce complex dynamical network models with coupling delays for both continuous- and discrete-time cases and then investigate their synchronization phenomena and criteria. Based on these new complex network models, we derive synchronization conditions for both delay-independent and delay-dependent asymptotical stabilities in terms of linear matrix inequalities (LMI). We finally use a network with a fixed delay and a specific coupling scheme as an example to illustrate the theoretical results.
A paradigm for nonlinear spatial-temporal processing, cellular neural networks (CNN), was created by inspiration from the cellular automata and neural networks. This article is an exploration of the important aspect of realizing Boolean functions by using standard CNN. A neat CNN truth table of n binary variables and an essential formula of an uncoupled CNN are discovered, and an effective method of realizing all linearly separable Boolean functions (LSBF) via CNN is proposed. Borrowed from biological concepts and terms, the parameter group in a CNN is a metaphor for gene which completely determines the dynamical properties of the CNN. The CNN gene bank, which consists of the family of all linearly separable Boolean genes (LSBG) that are associated with all the LSBF, can be easily determined and progressively established. An interesting phenomenon is that the number of LSBG with the von Neumann neighborhood is 94572, which is close to the number of genes existing in the human genome.
With the demand for energy efficiency in electro-hydraulic servo system (EHSS) increasing, the separate meter in and separate meter out (SMISMO) control system draws massive attention. In this paper, the SMISMO control system was decoupled completely into two subsystems by the proposed indirect adaptive robust dynamic surface control (IARDSC) method. Besides, a fast parameter estimation scheme was proposed to adapt to the parameter change for a better estimation performance. Also, a supply pressure controller with a disturbance observer and a supply flow rate controller with a grey model predictor were investigated and employed to save the power consumption. Finally, experimental results showed that the proposed IARDSC could achieve a good trajectory tracking performance with the fast parameter estimation. Meanwhile, the two energy saving techniques were validated.
This work intends to design, analyze and solve, from the systems control perspective, a complex, dynamic, and multiconstrained planning system for generating training plans for crew members of the NASA-led International Space Station. Various intelligent planning systems have been developed within the framework of artificial intelligence. These planning systems generally lack a rigorous mathematical formalism to allow a reliable and flexible methodology for their design, modeling, and performance analysis in a dynamical, time-critical, and multiconstrained environment. Formulating the planning problem in the domain of discrete-event systems under a unified framework such that it can be modeled, designed, and analyzed as a control system will provide a self-contained theory for such planning systems. This will also provide a means to certify various planning systems for operations in the dynamical and complex environments in space. The work presented here completes the design, development, and analysis of an intricate, large-scale, and representative mathematical formulation for intelligent control of a real planning system for Space Station crew training. This planning system has been tested and used at NASA-Johnson Space Center.
Research of delayed neural networks with varying self-inhibitions, interconnection weights, and inputs is an important issue. In the real world, self-inhibitions, interconnection weights, and inputs should vary as time varies. In this letter, we discuss a large class of delayed neural networks with periodic inhibitions, interconnection weights, and inputs. We prove that if the activation functions are of Lipschitz type and some set of inequalities, for example, the set of inequalities 3.1 in theorem 1, is satisfied, the delayed system has a unique periodic solution, and any solution will converge to this periodic solution. We also prove that if either set of inequalities 3.20 in theorem 2 or 3.23 in theorem 3 is satisfied, then the system is exponentially stable globally. This class of delayed dynamical systems provides a general framework for many delayed dynamical systems. As special cases, it includes delayed Hopfield neural networks and cellular neural networks as well as distributed delayed neural networks with periodic self-inhibitions, interconnection weights, and inputs. Moreover, the entire discussion applies to delayed systems with constant self-inhibitions, interconnection weights, and inputs.
