2,312 publications from this institution
In this letter, a novel control approach for complex networks with non-identical nodes is developed. This approach is based on inverse optimal pinning control to track a desired trajectory. The effectiveness of the control design is illustrated via simulations on a network with nodes being different chaotic systems.
A systematic circuit design approach is proposed for experimental verification of hyperchaotic 2, 3, 4-scroll attractors from a generalized Matsumoto-Chua-Kobayashi (MCK) circuit. The recursive formulas for system parameters are rigorously derived for improving the hardware implementation.
This Letter studies the generation of chaos from a linear autonomous system by employing a dynamical nonlinear feedback controller. The system setup is quite simple, and the only nonlinearity is a piecewise-quadratic function in the form of x|x|. Both computer simulation and circuit implementation are given to verify the chaos generated by this mechanism.
No abstract is provided for this article.
This paper is concerned with the uniform distribution of discrete spatiotemporal systems of the form where is a function, I is a bounded subset of and . Several new definitions on uniform distribution of this system are given. In particular, several new results for special cases of this system to be uniformly distributed are derived.
This report is a brief survey of some results using feedback to control two diverse models of cardiac electrical activity. A chaotic quadratic map has been used as a model of ventricular fibrillation. Here we describe simulations to control this map using a self-tuning feedback controller. We also demonstrate control of a one-dimensional circle map which has been used as a model of the cardiac arrhythmia known as modulated ventricular parasystole. We show that feedback control offers a straightforward and viable alternative for stabilization of these model systems in comparison to OGY or other control methods.
This article studies the controllability of multi-input/multioutput linear time-invariant (LTI) systems in a snapback interlayer coupling framework. Several necessary and sufficient conditions for the controllability of the three-layer snapback networks are established. In addition, controllability conditions about the superposition of the three-layer networks are obtained. These conditions are related to smaller scale factor networks, showing the effects of the interlayer coupling frameworks, intralayer network topologies, node dynamics, inner interactions, and external control inputs on the controllability of the snapback networks. Moreover, controllability conditions of the three-layer snapback networks are extended to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M$</tex-math></inline-formula> -layer setting. Finally, some examples are presented to illustrate the effectiveness of the controllability conditions.
<p style='text-indent:20px;'>Some ultimate bounds are derived for the complex Chen system.</p>
Universal perceptron (UP), a generalization of Rosenblatt's perceptron, is considered in this paper, which is capable of implementing all Boolean functions (BFs). In the classification of BFs, there are: 1) linearly separable Boolean function (LSBF) class, 2) parity Boolean function (PBF) class, and 3) non-LSBF and non-PBF class. To implement these functions, UP takes different kinds of simple topological structures in which each contains at most one hidden layer along with the smallest possible number of hidden neurons. Inspired by the concept of DNA sequences in biological systems, a novel learning algorithm named DNA-like learning is developed, which is able to quickly train a network with any prescribed BF. The focus is on performing LSBF and PBF by a single-layer perceptron (SLP) with the new algorithm. Two criteria for LSBF and PBF are proposed, respectively, and a new measure for a BF, named nonlinearly separable degree (NLSD), is introduced. In the sense of this measure, the PBF is the most complex one. The new algorithm has many advantages including, in particular, fast running speed, good robustness, and no need of considering the convergence property. For example, the number of iterations and computations in implementing the basic 2-bit logic operations such as AND, OR, and XOR by using the new algorithm is far smaller than the ones needed by using other existing algorithms such as error-correction (EC) and backpropagation (BP) algorithms. Moreover, the synaptic weights and threshold values derived from UP can be directly used in designing of the template of cellular neural networks (CNNs), which has been considered as a new spatial-temporal sensory computing paradigm.
No abstract is provided for this article.
This paper addresses the state estimation problem for a discrete-time uncertain system with a network of sensors, where the system is not necessarily observable by each sensor and deterministic uncertainties exist in the system matrices. A new robust estimator is designed for each sensor, using only its own and neighbor’s information, which is fully distributed. Moreover, a novel information fusion strategy is developed to guarantee the estimation performance, based on the collective observability of the sensor network, which greatly relaxes the technical assumption of the proposed estimator. Theoretically, it can be ensured that if the observed system is time-varying, the gains of the estimator will be bounded. Furthermore, if the system is time-invariant, these gains will be convergent. Subsequently, the estimation error covariance will be ultimately bounded if the observed system is quadratically bounded. In the end, the superiority of the proposed robust distributed state estimation algorithm is illustrated by several numerical simulation examples.
