Publications

In this paper, a simple and systematic control design method is proposed for a discrete-time Takagi-Sugeno (TS) fuzzy system, which employs the parallel distributed compensation (PDC) to determine the structure of a fuzzy controller so as to make all the Lyapunov exponents of the controlled TS fuzzy system strictly positive. This approach is proven to be mathematically rigorous for anticontrol of chaos for a TS fuzzy system, in the sense that any given discrete-time TS fuzzy system can be made chaotic by the designed PDC controller along with the mod-operation. A numerical example is included to visualize the anticontrol effect.

Thanks to their distinct synaptic plasticity and memory effects, memristors not only can mimic biological neuronal synapses but also can describe the influence of external electromagnetic radiation. This article proposes a novel memristive autapse-coupled neuron model (MACNM) using a locally active memristor as an autapse and simultaneously introducing a flux-controlled piecewise-nonlinear memristor to describe the external electromagnetic radiation. Theoretical analysis and numerical simulation results show that the MACNM is able to generate multiple numbers of grid multiscroll hidden attractors. Moreover, it can exhibit rich and complex hidden firing dynamics, including periodic spiking/bursting firing, chaotic spiking/bursting firing, as well as firing patterns transition. In particular, hidden firing multistability of five coexisting homogeneous chaotic bursting firing patterns with different offsets along the boosting route is discovered, giving raise to the interesting phenomenon of hidden homogeneous multistability. Finally, a circuit is designed to verify the physical feasibility of the abundant electrical activities in the proposed MACNM.

In this brief, a multi-carrier differential chaos shift keying system capable of mitigating channel noise is proposed. In this system, subcarriers are allocated to the reference and data signals in an optimal way, while noise reduction is achieved through reference diversity. To verify its performance, the bit error rate of the proposed system over multipath Rayleigh fading channels is derived, together with a detailed discussion about the determination of the optimal number of reference-carrying subcarriers. Extensive simulations are carried out for performance evaluation, which are further compared to other state-of-the-art systems. Results confirm the superior bit error rate performance of the proposed system, which has only small loss in energy and spectral efficiencies.

Gas-liquid two-phase flow is of great importance in various industrial processes. How to accurately measure the flow parameters in the gas-liquid two-phase flow remains a challenging problem. In this article, we develop a novel deep learning based soft measure technique to predict the gas void fraction, which is one key parameter in a gas-liquid two-phase flow. We conduct the vertical upward gas-liquid two-phase flow experiments to measure the flow signals by using the four-sector distributed conductance sensor. Then, we design a novel multitask-based temporal-channelwise convolutional neural network (MTCCNN) to predict the gas void fraction. In MTCCNN, we first utilize the decomposed convolutional block to extract temporal dependence and channel connection from fluid data. After further fusion by the dense layer, we apply multitask learning to make full use of the extracted features through both classification branch and gas void fraction prediction branch. We compare our MTCCNN with its variations to demonstrate the proposed improvements. We also present other competitive methods for comparisons, which shows that our MTCCNN presents a better performance in gas void fraction prediction.

A new edge centrality measure, connectivity rank index (CRI), is proposed based on the effect of an edge on the network algebraic connectivity. Compared with the existing indices, the CRI can determine the importance of a present edge as well as an absent edge. For large-scale networks, the algorithm based on original CRI definition has high-time complexity. Therefore, an approximation algorithm is designed using the eigenvector elements corresponding to the second smallest Laplacian eigenvalue. This algorithm can identify the most influential edges and the least influential ones easily, which reduces the time complexity from the exhaustive searching scheme with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(N^{5})$</tex-math> </inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(N^{3})$</tex-math> </inline-formula> in a network of size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$</tex-math> </inline-formula> . Some examples are shown to verify the effectiveness of the algorithm and the theoretical results.

In recent years, the analysis of human interaction data has led to the rapid development of graph embedding methods. Topological information is typically interpreted into embedded vectors or convolution kernels for link-based classification problems. This paper introduces a Bayesian graph embedding model for such problems, integrating network reconstruction, link prediction, and behavior prediction into a unified framework. Unlike the existing graph embedding methods, this model does not embed the topology of nodes or links into a low-dimensional space but sorts the probabilities of upcoming links and fuses the information of node topology and data domain via sorting. The new model integrates supervised transaction predictors with unsupervised link prediction models, summarizing local and global topological information. The experimental results on a financial trading dataset and a retweet network dataset demonstrate that the proposed feature fusion model outperforms the tested benchmarked machine learning algorithms in precision, recall, and F1-measure. The proposed learning structure has a fundamental methodological contribution and can be extended and applied to various link-based classification problems in different fields.

This paper investigates data-driven cooperative output regulation for continuous-time multi-agent systems with unknown network topology. Unlike existing studies that typically assume a known network topology to directly compute controller parameters, a novel approach is proposed that allows for the computation of the parameter without prior knowledge of the topology. A lower bound on the minimum non-zero eigenvalue of the Laplacian matrix is estimated using only edge weight bounds, enabling the output regulation controller design to be independent of global network information. Additionally, the common need for state derivative measurements is eliminated, reducing the amount of data requirements. Furthermore, necessary and sufficient conditions are established to ensure that the data are informative for cooperative output regulation, leading to the design of a distributed output regulation controller. For the case with noisy data, the bound of the output error is provided, which is positively correlated with the noise bound, and a distributed controller is constructed for the approximate cooperative output regulation. Finally, the effectiveness of the proposed methods is verified through numerical simulations.

We propose a negotiation strategy to address the effect of geography on the dynamics of naming games over small-world networks. Communication and negotiation frequencies between two agents are determined by their geographical distance in terms of a parameter characterizing the correlation between interaction strength and the distance. A finding is that there exists an optimal parameter value leading to fastest convergence to global consensus on naming. Numerical computations and a theoretical analysis are provided to substantiate our findings.