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A Smoothing Inertial Neural Network for Sparse Signal Reconstruction with Noise Measurements via Lp -L1 minimization

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circuits SYSTEMS AND SIGNAL PROCESSING 2022年第11期41卷 6295-6313页

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

In this paper, a smoothing inertial neural network (SINN) is proposed for the L-p-L-1 (1 >= p > 0) minimization problem, in which the objective function is non-smooth, non-convex, and non-Lipschitz. First, based on the smooth approximation technique, the objective function can be transformed into a smooth optimization problem, effectively solving the L-p-L-1 (1 >= p > 0) minimization model with non-smooth terms. Second, the Lipschitz property of the gradient that is smooth objective function is discussed. Then through theoretical analysis, the existence and uniqueness of the solution is discussed under the condition of restricted isometric property (RIP), and it is proved that the proposed SINN converges to the optimal solution of the minimization problem. Finally, the effectiveness and superiority of the proposed SINN are verified by the successful recovery performance under different pulse noise levels.

关键词： Smoothing inertial neural network L-p-L-1 (1 >= p > 0) minimization model sparse signal reconstruction

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Distributed event-triggered scheme for economic dispatch in power systems with uncoordinated step-sizes

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第16期13卷 3612-3622页

作者： Wang, Jinmeng Li, Huaqing Wang, Zheng Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China

Considering potential applications in power systems, economic dispatch problem (EDP) in time-varying balanced communication networks is studied, aiming to minimise the total cost of generating electricity. This is equivalent to dealing with the problem of optimising the sum of local functions while a single generator possesses only its local function. The variables of generators, satisfying some local constraints, are coupled by a linear constraint. In order to resolve the EDP, the authors design a fully distributed primal-dual optimisation algorithm with time-varying uncoordinated step-sizes. In consideration of saving computation and communication resources, an event-triggered scheme is introduced into the algorithm, based on which each generator is only allowed to interact with their neighbouring generators at some independent event-triggered sampling time instants. The proposed algorithm is able to achieve a linear convergence rate under the strong convexity and smoothness of the local objective functions. The Zeno-like behaviour is rigorously excluded, which means that the interval between any two consecutive sampling time instants of each generator is not less than two. Effectiveness of the algorithm and correctness of the theoretical analysis are verified by numerical experiments.

关键词： power generation dispatch convex programming distributed control convergence time-varying systems gradient methods multi-robot systems optimisation distributed event-triggered scheme consecutive sampling time instants local objective functions linear convergence rate independent event-triggered sampling time instants neighbouring generators communication resources computation time-varying uncoordinated step-sizes primal-dual optimisation algorithm authors design linear constraint local constraints single generator local function generating electricity total cost time-varying balanced communication networks EDP economic dispatch problem power systems

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A fast and efficient approach to color-image encryption based on compressive sensing and fractional Fourier transform

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第2期77卷 2191-2208页

作者： Zhang, Di Liao, Xiaofeng Yang, Bo Zhang, Yushu Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

This paper introduces a novel method of fast and efficient measurement matrices and random phase masks for color image encryption, in which Kronecker product (KP) is combined with chaotic map. The encryption scheme is based on two-dimension (2D) compressive sensing (CS) and fraction Fourier transform (FrFT). In this algorithm, the KP is employed to extend low dimension seed matrices to obtain high dimension measurement matrices and random phase masks. The low dimension seed matrices are generated by controlling chaotic map. The original image is simultaneously encrypted and compressed by the 2D CS, then re-encrypted with FrFT. The proposed encryption scheme fulfills high speed, low complexity and high security. Numerical simulation results demonstrate the excellent performance and security of the proposed scheme.

关键词： Image encryption Compressive sensing Kronecker product Skew tent map Fraction Fourier transform

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Observer-based event-triggered adaptive containment control for multiagent systems with prescribed performance

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nonlinear DYNAMICS 2022年第3期107卷 2345-2362页

作者： Xu, Rui Wang, Xin Zhou, Yuhao Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400044 Peoples R China

This paper focuses on the problem of the event-triggered adaptive containment control for a class of nonlinear multiagent systems (MASs) with prescribed performance and immeasurable states. First, the radial basis function neural networks (RBFNNs) are adopted to approximate the uncertain smooth nonlinear function, and the neural network-based state observer is designed to estimate the unmeasurable state. Besides, to reduce the control resource consumption and get a better balance between the system performance and network constraints, the switching threshold-based event-triggered control strategy is introduced. Based on this, the novel distributed containment controller is designed by utilizing the adaptive backstepping technique and the dynamic surface control (DSC) technique to guarantee that the output of each follower converges to the convex hull formed by multileader. Moreover, the containment errors can be converged to the prescribed boundary and all signals in closed-loop system are semi-global uniformly ultimately bounded (SGUUB) as well. Finally, the simulation examples are carried out to illustrate the efficiency of the proposed controller.

关键词： Adaptive containment control Event-triggered Prescribed performance State observer

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Distributed Optimization over General Directed Networks with Random Sleep Scheme

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2020年第10期18卷 2534-2542页

作者： Wang, Zheng Zheng, Lifeng Li, Huaqing Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

Distributed optimization aims at optimizing a global objective function which is described by a sum of local objective functions through local information processing and sharing. This paper studies the problem of distributed optimization over a network in which underlying graph is generally directed strongly connected. Most existing distributed algorithms require each agent to observe the gradient of the local objective function per iteration, which leads to heavy computational cost. A computation-efficient distributed optimization algorithm incorporating random sleep scheme is proposed by incorporating rescaling gradient technique to address the unbalancedness of the directed graph. The implementation of the proposed algorithm allows agents not only locally allocates the weights on the received information, but also independently decides whether to execute gradient observation at each iteration. Theoretical analysis verifies that the proposed algorithm is able to seek the optimal solution with probability one. Simulations are shown to demonstrate effectiveness of the proposed algorithm, show correctness of the theoretical analysis, and investigate the tradeoffs between convergence performance and computation cost.

关键词： Distributed convex optimization multi-agent systems random sleep scheme row-stochastic matrix

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Stability analysis of multimode oscillations in three coupled memristor-based circuits

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2016年第12期70卷 1569-1579页

作者： Wang, Yanjun Liao, Xiaofeng Southwest Univ Sch Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

In this paper, first we construct a simple flux -controlled memristor-based circuit. By means of the ladder type circuits coupled with inductors, furthermore, the three mutually coupled memristor oscillator circuits are designed. We mainly discuss dynamical behavior of three coupled memristor-based circuits, i.e., investigate the stability of the zero, single, double and multimode oscillations. Based on Krylov-Bogoliubov linearization method, the stability of the multimode oscillation for such circuits is determined in detail. Finally, computer simulations are conducted to verify the correctness of theoretical results. (C) 2016 Elsevier GmbH. All rights reserved.

关键词： Flux-controlled memristor Oscillator circuits Multimode oscillations Stability

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Edge-Based Stochastic Gradient Algorithm for Distributed Optimization

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2020年第3期7卷 1421-1430页

作者： Wang, Zheng Li, Huaqing Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

This paper investigates distributed optimization problems where a group of networked nodes collaboratively minimizes the sum of all local objective functions. The local objective function of each node is further set as an average of a finite set of subfunctions. This adjustment is motivated by machine learning problems with large training samples distributed and known privately to individual computational nodes. An augmented Lagrange (AL) stochastic gradient algorithm is presented to address the distributed optimization problem, which is integrated with the factorization of weighted Laplacian and local unbiased stochastic averaging gradient methods. At each iteration, only one randomly selected gradient of a subfunction is evaluated at a node, and a variance-reduced stochastic averaging gradient technique is applied to approximate the gradient of local objective function. Strong convexity of the local subfunction and Lipschitz continuity of its gradient are shown to ensure a linear convergence rate of the proposed algorithm in expectation. Numerical experiments on a logistic regression problem demonstrate the correctness of theoretical results.

关键词： Convergence Optimization Linear programming Convex functions Laplace equations Machine learning Training Distributed convex optimization machine learning augmented Lagrange stochastic averaging gradient

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Dopant-induced Morphology of Organic Semiconductors Resulting in High Doping Performance

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SMALL METHODS 2025年第1期9卷 e2400084页

作者： Guo, Jing Chen, Ping-An Yang, Shuzhang Wei, Huan Liu, Yu Xia, Jiangnan Chen, Chen Chen, Huajie Wang, Suhao Li, Wenwu Hu, Yuanyuan Hunan Univ Changsha Semicond Technol & Applicat Innovat Res I Coll Semicond Coll Integrated Circuits Changsha 410082 Peoples R China Shanxi Normal Univ Sch Phys & Informat Engn Taiyuan 030031 Peoples R China Hunan Univ Shenzhen Res Inst Shenzhen 518063 Peoples R China Hunan Univ Sch Phys & Elect Int Sci & Technol Innovat Cooperat Base Adv Displa Changsha 410082 Peoples R China Fudan Univ Inst Optoelect Dept Mat Sci Shanghai Frontiers Sci Res Base Intelligent Optoel Shanghai 200433 Peoples R China Natl Univ Def Technol Coll Aerosp Sci & Engn Sci & Technol Adv Ceram Fibers & Composites Lab Changsha Peoples R China Xiangtan Univ Coll Chem Key Lab Environm Friendly Chem & Applicat Minist Educ Xiangtan 411105 Peoples R China Univ Littoral Cote dOpale Unite Dynam & Struct Mat Mol UDSMM 145 Ave Maurice Schumann F-59140 Dunkerque France

Doping plays a crucial role in modulating and enhancing the performance of organic semiconductor (OSC) devices. In this study, the critical role of dopants is underscored in shaping the morphology and structure of OSC films, which in turn profoundly influences their properties. Two dopants, trityl tetrakis(pentafluorophenyl) (TrTPFB) and N,N-dimethylanilinium tetrakis(pentafluorophenyl)borate (DMA-TPFB), are examined for their doping effects on poly(3-hexylthiophene) (P3HT) and PBBT-2T host OSCs. It is found that although TrTPFB exhibits higher doping efficiency, OSCs doped with DMA-TPFB achieve comparable or even enhanced electrical conductivity. Indeed, the electrical conductivity of DMA-TPFB-doped P3HT reaches over 67 S cm-1, which is a record-high value for mixed-solution-doped P3HT. This can be attributed to DMA-TPFB inducing a higher degree of crystallinity and reduced structural disorder. Moreover, the beneficial impact of DMA-TPFB on the OSC films' morphology and structure results in superior thermoelectric performance in the doped OSCs. These findings highlight the significance of dopant-induced morphological and structural considerations in enhancing the film characteristics of OSCs, opening up a new avenue for optimization of dopant performance. Two dopants for organic semiconductors (OSCs) are investigated and compared with respect to their doping efficacy and the structural changes they induce. It is demonstrated that dopants possessing lower intrinsic doping strength can result in high semiconductor performance when they induce advantageous structural changes. The findings emphasize the critical role of considering dopant-induced structural modifications in OSC films. image

关键词： conductivity doping organic semiconductors structural changes thermoelectric performance

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Multikernel Adaptive Filters Under the Minimum Cauchy Kernel Loss Criterion

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IEEE ACCESS 2019年 7卷 120548-120558页

作者： Shi, Wei Xiong, Kui Wang, Shiyuan Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

The Cauchy loss has been successfully applied in robust learning algorithms in the presence of large outliers, but it may suffer from performance degradation in complex nonlinear tasks. To address this issue, by transforming the original data into the reproducing kernel Hilbert spaces (RKHS) with the kernel trick, a novel Cauchy kernel loss is developed in such a kernel space. Based on the minimum Cauchy kernel loss criterion, the multikernel minimum Cauchy kernel loss (MKMCKL) algorithm is proposed by mapping the input data into the multiple RKHS. The proposed MKMCKL algorithm can provide the performance improvement of the kernel adaptive filter (KAF) based on a single kernel, and also improve the stability of the multikernel adaptive filter based on the quadratic loss in impulsive noises, efficiently. To further curb the growth of network of MKMCKL, a novel sparsification method is presented to prune redundant data, thus reducing its computational and storage burdens. Simulations on different nonlinear applications illustrate the performance superiorities of the proposed algorithms in impulsive noises.

关键词： Multikernel algorithm robust adaptive filter minimum Cauchy kernel loss criterion kernel selection sparsification

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Stability Analysis of Stochastic Delayed Differential Systems with State-Dependent-Delay Impulses: Application of Neural Networks

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COGNITIVE COMPUTATION 2022年第2期14卷 805-813页

作者： Zhang, Wei Huang, Junjian Southwest Univ Dept Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

The previously studied stochastic delayed nonlinear systems with constant delayed or time-varying delayed impulsive controller, while we study stochastic delayed nonlinear systems under state-dependent-delayed impulsive controller. The difficulty is how to determine the time of impulses occurrence. Employing the Halanay differential inequality, Ito's formula, the average impulsive interval, impulsive control theory, comparison properties, several effective conditions ensuring stability of stochastic delayed nonlinear systems under state-dependent delayed impulsive controller are derived. This paper contributed to the stability analysis of delayed impulsive nonlinear systems with stochastic perturbation, which the impulsive involved delay is state dependent. We have developed exponential stability of delayed nonlinear systems with state-dependent-delay impulses and stochastic disturbance in this paper. In the future, more new methods should also be proposed. At the same time, we will consider the nonlinear systems with unbounded delays.

关键词： Stochastic delayed differential systems Stability State-dependent delayed impulsive

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