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A Chaotic Oscillator Based on HP Memristor Model

MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-12页

作者： Wang, Guangyi Cui, Mingzhang Cai, Bozhen Wang, Xiaoyuan Hu, Tiling Hangzhou Dianzi Univ Inst Modern Circuits & Intelligent Informat Key Lab RF Circuits & Syst Minist Educ China Hangzhou 310018 Zhejiang Peoples R China

This paper proposes a simple autonomous memristor-based oscillator for generating periodic signals. Applying an external sinusoidal excitation to the autonomous system, a nonautonomous oscillator is obtained, which contains HP memristor model and four linear circuit elements. This memristor-based oscillator can generate periodic, chaotic, and hyperchaotic signals under the periodic excitation and an appropriate set of circuit parameters. It also shows that the system exhibits alternately a hidden attractor with no equilibrium and a self-excited attractor with a line equilibrium as time goes on. Furthermore, some specialties including burst chaos, irregular periodic bifurcations, and nonintermittence chaos of the circuit are found by theoretical analysis and numerical simulations. Finally, a discrete model for the HP memristor is given and the main statistical properties of this memristor-based oscillator are verified via DSP chip experiments and NIST (National Institute of Standards and Technology) tests.

关键词： MEMRISTORS CIRCUIT elements CHAOS theory DIGITAL signal processing ATTRACTORS (Mathematics) EQUILIBRIUM

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A Sparse Recovery Algorithm Using the Neurodynamic System With Predefined Time Convergence

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IEEE TRANSACTIONS ON circuits AND SYSTEMS II-EXPRESS BRIEFS 2023年第8期70卷 3029-3033页

作者： Zhu, Yiyue He, Xing Southwest Univ Westa Coll Chongqing 400715 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

Compressed sensing aims to compress sparse signals losslessly into significantly smaller samples and recover them if needed. One of the most critical approaches to recovering the sparse signal is minimizing $l_nonlinear$ -norm, the convex relaxation of $l_nonlinear$ -norm with the limit of linear measurement. However, sometimes such as the recovery of piecewise constant sparse signal, the need for controlling the convergence time has become an inevitable problem to solve. To solve this problem, the predefined time pseudo-inverse locally competitive algorithm (PPLCA) based on a nonlinear neurodynamic system is proposed in this brief, which is exceptionally effective if the signal is piecewise constant. Further, under the restricted isometry property (RIP), the predefined-time convergence rate of the proposed PPLCA is proven using Lyapunov's stability theory. Finally, the numerical simulations show that the PPLCA could converge to the stationary point within the predefined time.

关键词： Index Terms-Compressed sensing l1-norm predefined local competitive algorithm predefined time local competitive algo-rithm sliding mode control technique

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A fixed-time gradient algorithm for distributed optimization with inequality constraints

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NEUROCOMPUTING 2023年 532卷 106-113页

作者： He, Xing Wei, Boyu Wang, Hui Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Chongqing Normal Univ Sch Math Sci Chongqing 401331 Peoples R China

In this paper, a distributed fixed time gradient algorithm for neurodynamic systems is proposed for solv-ing optimization problems with local inequality constraints. The algorithm is designed using fixed time theory and sliding model control techniques, where each agent has a local objective function known only to itself, and the optimal solution of each local objective function sum can be obtained in a fixed time by the information interaction between neighbors under the condition of local inequality constraints. In addition, the upper bound of the fixed time can be obtained and it is proved theoretically that the upper bound of the fixed time is independent of the initial value. Finally, the stability and effectiveness of the algorithm are verified by numerical examples.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Gradient algorithm Fixed time convergence Distributed optimization Load sharing control

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Distributed neurodynamic optimization for multi-energy management with time-varying external disturbances considering time-varying emission limitations and load demand in multi-microgrid

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ISA TRANSACTIONS 2023年第1期139卷 337-356页

作者： Li, Wenwen Wang, Hui He, Xing Southwest Univ Sch Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Chongqing Normal Univ Sch Math Sci Chongqing 401331 Peoples R China

An emerging time-varying distributed multi-energy management problem (MEMP) considering time-varying load and emission limitations for resisting time-varying external disturbances and communication time delays in the multi-microgrid (MMG) system is investigated. Each microgrid (MG) contains some smaller microgrids (SMGs), which are connected by energy routers (ERs) of the system and can monitor energy in real-time with each other. In addition, a time-varying multi-energy management optimization model (MEMOM) is proposed in this paper in order to minimize the total cost of the MEMP which considers environmental cost, renewable energy cost and fuel cost. Furthermore, time-varying distributed neurodynamic optimization algorithms are proposed for solving the above MEMP based on consensus theory and sliding mode control technique. Compared with the optimization algorithms which consist of symbolic functions proposed in traditional energy management problems, algorithms consisting of hyperbolic tangent functions proposed in this paper can effectively reduce the oscillation of the algorithms and improve the stability of algorithms. Furthermore, the algorithm can converge the optimal trajectory of optimization problems with time-varying external disturbances and communication time delays. Meanwhile, the stability and convergence of the algorithms are proved theoretically by constructing appropriate Lyapunov functions. Finally, the performance evaluation re-sults of numerical simulations show that the proposed algorithms can efficiently handle energy trading under time-varying load and maintain excellent stability with time-varying external disturbances and communication time delays.(c) 2023 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Multi-energy management Multi-microgrid Time-varying external disturbances Communication time delays Time-varying distributed neurodynamic optimization algorithm Time-varying emission limitations

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On Physical-Layer Security in Underlay Cognitive Radio Networks with Full-Duplex Wireless-Powered Secondary System

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IEEE ACCESS 2016年 4卷 3887-3893页

作者： Zhang, Jiliang Pan, Gaofeng Wang, Hui-Ming Southwest Univ Sch Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Minist Educ Key Lab Intelligent Networks & Network Secur Xian 710049 Peoples R China

In this paper, we consider an underlay cognitive radio system, in which a source in a secondary system transmits information to a full-duplex (FD) wireless-powered destination node in the presence of an eavesdropper. In particular, the destination node is equipped with a single receiving antenna and a single transmitting antenna to enable FD operation. The receiving antenna can simultaneously receive information and energy from the source through power-splitter architecture. The received energy is then used in the transmitting antenna to send jamming signals to degrade the eavesdropper's decoding capacity. Upper and lower bounds of probability of strictly positive secrecy capacity (SPSC) have been derived. Numerical results show that under the condition that the interference from the source and destination at primary user's receiver is smaller than the interference temperature limit, upper and lower bounds merge together to become the exact SPSC.

关键词： Cognitive radio networks full-duplex simultaneous wireless information and power transfer probability of strictly positive secrecy capacity

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Diffusion generalized maximum correntropy criterion algorithm for distributed estimation over multitask network

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DIGITAL SIGNAL PROCESSING 2018年 81卷 16-25页

作者： Chen, Feng Li, Xinyu Duan, Shukai Wang, Lidan Wu, Jiagui Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Southwest Univ Sch Math & Stat Chongqing 400715 Peoples R China Key Lab Nonlinear Circuits & Intelligent Informat Chongqing 400715 Peoples R China

Adopting mean-square error (MSE) criterion, distributed estimation algorithms achieve desirable performance if the background noise is drawn from the Gaussian distribution. However, these algorithms may degrade considerably in non-Gaussian situations, especially for the impulsive-noise. The diffusion generalized maximum correntropy criterion (D-GMCC) algorithm is proposed in this article to address this problem. Furthermore, we contribute in multiple tasks problem, which differs from single-task estimation problem. The relatedness of tasks among nodes is also studied to uncover its impact on estimation performance. Simulations illustrate that the proposed algorithm achieves desirable performance and outperforms other related methods. The results on the mean and mean square stability analysis of the proposed algorithm are also provided. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Impulsive interference Distributed estimation Generalized maximum correntropy criterion Multitask

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Distributed generalized Nash equilibrium seeking: A backward-reflected-forward-backward-based algorithm

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2024年第1期361卷 150-163页

作者： Zheng, Zuqing Li, Huaqing Niu, Youcheng Su, Enbing Feng, Liping Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Xinzhou Teachers Univ Dept Comp Sci Xinzhou 034000 Peoples R China

This paper studies non-cooperative games, in which each player's local objective function depends on its own decisions as well as those of other players. In these games, each player's local objective function is differentiable, its decision is constrained by a local feasibility constraint, and the decisions of all players are coupled with an equality constraint. By analyzing the variational problem of the games, a distributed generalized Nash equilibrium seeking algorithm with fixed step sizes is developed based on the backward-reflected-forward-backward splitting. Each player performs a backward step followed by a forward-backward step, and the pseudo -gradient is evaluated at a reflection term. Moreover, the convergence of the proposed distributed algorithm is analyzed under standard assumptions using the operator theory and the convex analysis theory. Finally, the simulation results verify the effectiveness of the algorithm and the correctness of the theoretical analysis.

关键词： Generalized Nash equilibrium Distributed algorithm Backward-reflected-forward-backward

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A Triboelectric Sensor with Double Bubble Structure Applied in a High Security Double Lock System

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ACS SENSORS 2023年第12期8卷 4615-4624页

作者： Ouyang, Wei Luo, Fangyuan Yao, Youbin Qu, Bingbing Feng, Changhao Xie, Yiyuan Chen, Bin Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuit & Intelligent Chongqing 400715 Peoples R China

With more attention on personal privacy and the need for a security defense, it is necessary to design an intelligent lock system with a higher security performance. Here, a novel high security double lock system integrating triboelectric nanogenerators (TENGs) with a double bubble structure (DB-TENG) and deep learning models is proposed. The TENG as a self-powered sensor is developed using silicone rubber and copper foil. By optimizing the thickness of the top layer film, surface microstructure, the size of the air bubble, and design of the double bubble structure, the sensitivity of the DB-TENG reaches 19.08 V/kPa. For the feasibility study, the sensor is fabricated to a smart belt to collect respiratory behaviors as a respiratory code. A Long Short-Term Memory network is adopted to identify four typical respiratory signals with an average accuracy of 97.00%. The system is deployed on a Raspberry Pi to determine whether the user is permitted through both the collected respiratory code and the related face image and will send an alarm message if one of the two does not match. It is worth mentioning that users can send alarm signals undiscovered by controlling their respiratory signals. Therefore, the proposed system has superb potential in security demanding environments.

关键词： triboelectric sensor double bubble structure respiration recognition deep learning doublelock system

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A Non-convex Dispatch Problem with Generator Constraints Using Neural Network and Particle Swarm Optimization

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2020年第1期44卷 185-196页

作者： Duan, Yuzhu He, Xing Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China

In this paper, we consider an economic emission dispatch problem consisting of the thermal generators with valve point effect and the wind turbines. Due to the uncertainty of wind power generation, the cost of reserve capacity penalty and the cost of wind abandoning are introduced into the wind generation model. Moreover, the aforementioned problem is subject to the capacity constraint and supply demand balance constraint which include the power loss of thermal power generation and wind power generation. In modeling the wind generation, the wavelet neural network is introduced to predict the wind power in order to get more accurate power data in a day. Next, the penalty function method is used to deal with the supply demand balance constraint;then, the local optima is found using projection neural network, and particle swarm optimization is utilized to update initial points to obtain the global optima. Finally, the power distribution of ten units with different load demands within 24 h is described.

关键词： Projection neural network Particle swarm optimization Wavelet neural network

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A Proportionate Maximum Total Complex Correntropy Algorithm for Sparse Systems

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circuits SYSTEMS AND SIGNAL PROCESSING 2024年第10期43卷 6415-6436页

作者： Huang, Sifan Liu, Junzhu Qian, Guobing Wang, Xin Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Sichuan Univ Sch Cyber Sci & Engn Chengdu 610065 Peoples R China

While the practical application of adaptive filters has indeed garnered substantial attention, two pressing issues persist that have a profound impact on their performance-system sparsity and the presence of contaminated Gaussian impulsive noise. In this research paper, we propose a novel approach to tackle both of these issues simultaneously by introducing the concept of a proportionate matrix. Specifically, we present a proportionate maximum total complex correntropy algorithm based on the errors-in-variables model. The paper presents a theoretical analysis of the steady-state weight error power under the influence of impulsive noise. Furthermore, it discusses the performance comparison in system identification and highlights the robustness of the proposed algorithm. To validate its effectiveness, a simulation involving stereophonic acoustic echo cancellation is conducted, and the results confirm the clear advantages of the proposed Proportionate Maximum Total Complex Correntropy algorithm.

关键词： Sparse system Impulsive noise Proportionate matrix Errors-in-variables model Stereophonic acoustic echo cancellation

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