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A Neurodynamic Optimization Approach for L₁ Minimization with Application to Compressed Image Reconstruction

INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2021年第1期30卷 2140007-2140007页

作者： Dai, Chengchen Che, Hangjun Leung, Man-Fai 1919 Technol Co Ltd Chengdu 610000 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400700 Peoples R China Open Univ Hong Kong Sch Sci & Technol Hong Kong Peoples R China

This paper presents a neurodynamic optimization approach for l(1) minimization based on an augmented Lagrangian function. By using the threshold function in locally competitive algorithm (LCA), subgradient at a nondifferential point is equivalently replaced with the difference of the neuronal state and its mapping. The efficacy of the proposed approach is substantiated by reconstructing three compressed images.

关键词： Neurodynamic optimization l(1) minimization compressed image reconstruction

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FLS-based finite-time synchronization of delayed memristive neural networks with interval parameters and nonlinear couplings

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2019年 533卷 121890-000页

作者： Liu, Yicheng Liao, Xiaofeng Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

This paper is concerned with the problem of the finite-time drive-response synchronization of a general class of delayed memristive neural networks (DMNN) with interval parameters, fuzzy logical system-based model and nonlinear couplings. An Takagi-Sugeno (TS) fuzzy logic system (FLS) is introduced with memristor-based neuromorphic network which exhibits a tremendous advantage in terms of building artificial brain, and due to the unexpected parameter mismatch in DMNN when different initial conditions are selected, the Filippov-framework and the interval matrix method are considered to reduce the conservativeness of the finite-time synchronization criteria. By utilizing the novel discontinuous controller with the discontinuous state feedback and the adaptive term in the Lyapunov functional framework, some concise conditions are acquired to guarantee the finite-time drive-response synchronization of DMNN. In addition, it is shown that a unified condition for the finite-time synchronization of fuzzy-based DMNN with nonlinear couplings is derived. Finally, several simulated examples are also presented to demonstrate the correctness of the theoretical results. (C) 2019 Published by Elsevier B.V.

关键词： Delayed memristive neural networks(DMNN) Fuzzy logical system(FLS) Interval parameters nonlinear couplings Discontinuous controller

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Diffusion Sparse Sign Algorithm with Variable Step-Size

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circuits SYSTEMS AND SIGNAL PROCESSING 2019年第4期38卷 1736-1750页

作者： Chen, Feng Liu, Xiaoxia 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 Chongqing Collaborat Innovat Ctr Brain Sci Chongqing 400715 Peoples R China

In this paper, we propose the diffusion sparse sign algorithm with variable step-size for distributed estimation in sparse and impulsive interference environments. Firstly, we address the problem of in-network distributed estimation for sparse vectors under the impulsive noise environment. In order to exploit the sparsity of the vector of interest, we incorporate the sparse norms (l1-norm and RWl1-norm) into the cost function of the standard diffusion sign algorithm, which accelerates the convergence speed of zero or near-zero components. In addition, we propose the adaptive variable step-size to further improve the convergence rate of the proposed algorithm. The variable step-size is derived by the correlation entropy, which contains a modified Gaussian kernel function and is robust to impulsive noise. In this paper, every node combines its correlation entropy function with the information of its neighborhood to drive the variable step-size at each iteration. Simulation results show that the proposed algorithm outperforms the standard diffusion SA in the sparse and impulsive system and the convergence rate of the proposed algorithm is faster than constant step-size algorithms.

关键词： Diffusion Sparsity Variable step-size Impulsive noise Sign algorithm

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Convergence Analysis of a Distributed Optimization Algorithm with a General Unbalanced Directed Communication Network

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2019年第3期6卷 237-248页

作者： Li, Huaqing Lu, Qingguo Huang, Tingwen Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China Texas A&M Univ Qatar Sci Program Doha 23874 Qatar

In this paper, we discuss a class of distributed constrained optimization problems in power systems where the target is to optimize the sum of all agents' local convex objective functions over a general unbalanced directed communication network. Each local convex objective function is known exclusively to a single agent, and the agents' variables are constrained to global coupling linear constraint and individual box constraints. To collaboratively solve the optimization problems, existing distributed methods mostly require the communication network to be balanced or have the knowledge of in-neighbors' out-degree for all agents, which are quite restrictive and hardly inevitable in practical applications. In contrast, we investigate a novel distributed primal-dual augmented (sub)gradient algorithm which utilizes a row-stochastic matrix (does not need each agent to know its in-neighbors out-degree) and employs uncoordinated step-sizes, and yet exactly converges to the optimal solution over a general unbalanced directed communication network. Under the assumptions of the strong convexity and smoothness on the aggregate objective functions, it is proved that the algorithm geometrically converges to the optimal solution if the uncoordinated step-sizes do not exceed the upper bound. An explicit analysis for the convergence rate of the proposed algorithm is also characterized. To manifest effectiveness and applicability of the proposed algorithm, three case studies are presented to solve two practical problems in power systems.

关键词： Distributed constrained optimization unbalanced directed network primal-dual (sub)gradient algorithm uncoordinated step-sizes power systems

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Self-sustained oscillation in a memristor circuit

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nonlinear DYNAMICS 2019年第2期96卷 1267-1281页

作者： Liao, Xingran Mu, Nankun Sichuan Univ Sch Math Chengdu 610065 Sichuan Peoples R China Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China

In this paper, we consider dynamical behavior of a comparatively simple self-oscillating circuit only with an inductor, a capacitor and a memristor, but this circuit can produce a self-sustained oscillation. By applying the point transformation method and the multiple-scale method, we carefully analyze dynamics of this circuit and find there exists a periodic solution. In our study, the two cases for the flux-charge relation of a memristor, i.e., continuous piecewise-linear function and discontinuous function, are considered. By using the above two approaches, we analytically obtain the approximated period and amplitude of the periodic solution. Some numerical examples also verify the correctness of theoretical analysis.

关键词： Memristor Piecewise-linear function Discontinuous function Oscillation The point transformation method The multiple-scale method

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Distributed Fast Supervised Discrete Hashing

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IEEE ACCESS 2019年 7卷 90003-90011页

作者： Liu, Zhifeng Chen, Feng Duan, Shukai Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Key Lab Nonlinear Circuits & Intelligent Informat Chongqing 400715 Peoples R China Chongqing Collaborat Innovat Ctr Brain Sci Chongqing 400715 Peoples R China

Hash-based learning has attracted considerable attention due to its fast retrieval speed and low computational cost for the large-scale database. Compared with unsupervised hashing, supervised hashing achieves higher retrieval accuracy generally by leveraging supervised information. Most existing supervised hashing methods, such as supervised discrete hashing (SDH) and fast SDH (FSDH), are concerned more with the centralized setting. SDH regresses the hash code to its corresponding label, rather FSDH regressing each label to its corresponding hash code. However, in many realistic applications, large amounts of data are usually distributed across different sites. Thus, supervised distributed hashing (SupDisH), which is based on the distributed framework and supervised learning, has been proposed and liberates the limitations of centralized hashing. In this paper, based on FSDH, we propose the distributed fast supervised discrete hashing (DFSDH), which both inherits the excellent retrieval performance of SupDisH and gets significant enhancement in efficiency. Specifically, FSDH is introduced into a distributed framework, in which the centralized hash learning model is shared by all agents. Meanwhile, consistency constraints are introduced to ensure that multiple agents deal with distributed hash learning in parallel. For each agent, an alternate iterative procedure is employed to obtain high-quality binary codes and hashing function. The extensive experiments demonstrate that DFSDH is competitive to most centralized supervised hashing methods and existing distributed hashing methods.

关键词： Distributed hashing fast discrete hashing supervised learning

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Stability of nonlinear variable-time impulsive differential systems with delayed impulses

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nonlinear ANALYSIS-HYBRID SYSTEMS 2021年 39卷 100970-100970页

作者： He, Zhilong Li, Chuandong Cao, Zhengran Li, Hongfei Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Xinjiang Univ Finance & Econ Sch Finance Urumqi 830012 Peoples R China Peking Univ State Key Lab Turbulence & Complex Syst Dept Mech & Engn Sci BIC ESATColl Engn Beijing 100871 Peoples R China

This paper investigates the stability of nonlinear variable-time impulsive differential systems with delayed impulses. By using the comparison principle, the Lyapunov method and inequality techniques, some sufficient conditions for quasi-uniformly asymptotic stability and quasi-exponential stability of a given solution of variable-time impulsive differential systems with delayed impulses are established. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： nonlinear system Variable-time impulses Delayed impulses Quasi-uniformly asymptotic stability Quasi-exponential stability

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Distributed Projection Subgradient Algorithm Over Time-Varying General Unbalanced Directed Graphs

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2019年第3期64卷 1309-1316页

This paper is concerned with a general class of distributed constrained optimization problems over a multiagent network, where the global objective function is represented by the sum of all local objective functions. Each agent in the network only knows its own local objective function, and is restricted to a global nonempty closed convex set. We discuss the scenario where the communication of the whole multiagent network is expressed as a sequence of time-varying general unbalanced directed graphs. The directed graphs are required to be uniformly jointly strongly connected and the weight matrices are only rowstochastic. To collaboratively deal with the optimization problems, existing distributed methods mostly require the communication graph to be fixed or balanced, which is impractical and hardly inevitable. In contrast, we propose a new distributed projection subgradient algorithm which is applicable to the time-varying general unbalanced directed graphs and does not need each agent to knowits in-neighbors' out-degree. When the objective functions are convex and Lipschitz continuous, it is proved that the proposed algorithm exactly converges to the optimal solution. Simulation results on a numerical experiment are shown to substantiate feasibility of the proposed algorithm and correctness of the theoretical findings.

关键词： Distributed convex optimization multiagent network projection subgradient algorithm row-stochastic matrix time-varying unbalanced directed graphs

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Performance Comparison of nonlinear Kalman Filters in Epidemic Tracking on Networks

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IEEE SYSTEMS JOURNAL 2020年第4期14卷 5475-5485页

作者： Wang, Wanli Tse, Chi K. Wang, Shiyuan Hong Kong Polytech Univ Dept Elect & Informat Engn Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Kowloon Hong Kong Peoples R China City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China

The problem of tracking epidemic spreading on networks is relevant to the control of morbidity. The transmission dynamics of an epidemic can be described by a simple compartmental model. Specifically, the estimation of epidemic spreading on networks can be achieved by a nonlinear Kalman filter, which is a tool for state estimation of nonlinear systems. In this article, epidemic spreading on networks is described by compartmental models, such as susceptible-infected-susceptible, susceptible-infected-recovered, and susceptible-infected-recovered-susceptible models. The dynamics of epidemic spreading on homogeneous networks, including Erdos-Renyi network, Watts-Strogatz network, and Newman-Watts network, are estimated by several nonlinear Kalman filters, including extended Kalman filter, unscented Kalman filter, and third-degree and fifth-degree cubature Kalman filters. The performance comparison in terms of accuracy and stability forms a guideline of utilizing nonlinear Kalman filters for tracking epidemic spreading. The theoretical analysis has been validated through numerical experiments.

关键词： Kalman filters Erbium State estimation Complex networks Stability analysis Sociology Statistics Compartmental model epidemic spreading homogeneous network nonlinear Kalman filter

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Improved Consensus Conditions for Multi-Agent Systems With Uncertain Topology: The Generalized Transition Rates Case

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2020年第3期7卷 1158-1169页

作者： Wang, Xin Wang, Hui Li, Chuandong Huang, Tingwen Kurths, Juergen Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Potsdam Inst Climate Impact Res D-14473 Potsdam Germany Humboldt Univ Inst Phys D-12489 Berlin Germany Chongqing Normal Univ Sch Math Sci Chongqing 401331 Peoples R China Texas A&M Univ Qatar Doha 23874 Qatar

This paper is dedicated to addressing the consensus issues for a class of multi-agent systems (MASs) subjected to Markovian switching interaction topology and time-varying input delay. In line with this consideration, we first propose a modified reciprocally convex inequality, named as delay-dependent reciprocally convex inequality to obtain a tighter upper bound about the time-derivative of Lyapunov-Krasovskii functional. As a result, an improved condition with less conservatism stated elegantly in terms of linear matrix inequalities is established such that the MASs with Markovian switching topology and time varying input delay can approach the unified state. Further, the general cases of Markovian switching topology where each transition rate is completely unknown or just its estimate value is known are systematically investigated as well. Moreover, to make our results more meaningful, several comparison results concerning the admissible input delay against other related ones are also presented, which yield that our consensus conditions significantly improve the existing ones advocated thus far. Finally, we verify the validity, the effectiveness, and the practical applicability of our results in the simulation examples.

关键词： Delays Topology Switches Network topology Symmetric matrices Multi-agent systems Upper bound Consensus input delay delay-dependent reciprocally convex inequality (DRCI) incomplete transition rates

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