Xing He

Southwest University in Chongqing | SWU · School of Electronics and Information Engineering

The Nesterov accelerated dynamical approach serves as an essential tool for addressing convex optimization problems with accelerated convergence rates. Most previous studies in this field have primarily concentrated on unconstrained smooth convex optimization problems. In this paper, on the basis of primal-dual dynamical approach, Nesterov accelera...

Variational inequalities (VIs) have become a general framework for efficient problems in various fields such as optimal control and nonlinear programming. Some real problems in application areas such as signal processing and network resource allocation can be transformed into VIs. VIs have been frequently solved using neurodynamic approaches, which...

This paper studies the bipartite synchronization of signed networks with time-varying delays based on T-S fuzzy system, where the edges between nodes can be positive or negative. Assume that the signed graph of the network is structurally balanced. Firstly, the signed network system is described by T-S fuzzy model and then the control controller is...

In this paper, a novel projection neural network (PNN) for solving the $L_{1}$ -minimization problem is proposed, which can be applied to sparse signal reconstruction and image reconstruction. First, a one-layer PNN is designed with the projection matrix and the projection operator, which is shown to be stable in the Lyapunov sense and converges...

In this paper, a distributed smoothing accelerated projection algorithm (DSAPA) is proposed to address constrained non-smooth convex optimization problems over undirected multi-agent networks in a distributed manner, where the objective function is free of the assumption of Lipschitz gradient or strong convexity. First, based on a distributed exact...

In this brief, two arbitrary-time stable gradient flows are proposed to solve the continuous-time optimization problems based on nonlinear neurodynamic system. In contrast to the conventional gradient flow methods, the proposed gradient flows adopt the arbitrary-time sliding mode controller. It is shown that the proposed gradient flows converge to...

This article investigates a class of systems of nonlinear equations (SNEs). Three distributed neurodynamic models (DNMs), namely a two-layer model (DNM-I) and two single-layer models (DNM-II and DNM-III), are proposed to search for such a system’s exact solution or a solution in the sense of least-squares. Combining a dynamic positive definite matr...

In this paper, we investigate several distributed inertial algorithms in continuous and discrete time for solving resource allocation problem (RAP), where its objective function is convex or strongly convex. First, the original RAP is equivalently transformed into a distributed unconstrained optimization problem by introducing an auxiliary variable...

This paper considers the \(L_1\)-minimization problem for sparse signal and image reconstruction by using projection neural networks (PNNs). Firstly, a new finite-time converging projection neural network (FtPNN) is presented. Building upon FtPNN, a new fixed-time converging PNN (FxtPNN) is designed. Under the condition that the projection matrix s...

This paper investigates two distributed accelerated primal-dual neurodynamic approaches over undirected connected graphs for resource allocation problems (RAP) where the objective functions are generally convex. With the help of projection operators, a primal-dual framework, and Nesterov’s accelerated method, we first design a distributed accelerat...

In this paper, two neurodynamic algorithms and the corresponding Field-Programmable-Gate-Array (FPGA) implementation scheme are presented, respectively. Firstly, based on Lagrange programming neural networks (LPNN) and sliding mode control technique, an algorithm with finite-time convergence and an algorithm with fixed-time convergence is proposed...

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 $l_{1}$ -norm, the convex relaxation of $l_{0}$ -norm with the limit of linear measurement. However, sometimes such as the recovery of piec...

In this paper, bipartite synchronization, as a distinctly important part of studying the Lur’e system, is investigated under quantized control. And the exchange relations are capable of being cooperative or competitive in these adjacent nodes. Put the case that the network remains balanced in structure. Simultaneously, bring some conditions and tra...

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 ene...

This paper investigates the energy management problem of the energy Internet under time-varying conditions . In the context of coupled multi-energy networks, the energy Internet is considered to be composed of multiple energy bodies and requires collaborative planning of multiple energy networks. A model for distributed energy management with a non...

Existing circuits for composite optimization problems tend to ignore the structure of nonsmooth objectives and lead to less practicability. To this end, with a proximal projection neural network (PPNN) and an inertial proximal projection neural network (IPPNN), this paper presents two novel analog circuit frameworks in presence of a nonsmooth term,...

Sparsity has been extensively employed in multimedia sensing and computing in consumer electronics, signal and image processing, depth video codec, adaptive sparse-type equalizer, blind speech separation, and machine learning. Throughout this paper, we propose a novel distributed projection neurodynamic approach for solving the Basis Pursuit (BP) w...

Sparse optimization problems have been successfully applied to a wide range of research areas, and useful insights and elegant methods for proving the stability and convergence of neurodynamic algorithms have been yielded in previous work. This article develops several neurodynamic algorithms for sparse signal recovery by solving the $\ell_{1}$ r...

This technical note presents a unified single-layer inverse-free neurodynamic network (SINN) to tackle absolute value equations (AVEs). A new global error bound for AVE is established and it is tighter than existing error bounds. Moreover, under different parameter conditions, the global asymptotic, finite-time and fixed-time convergence of the pro...

In this paper, a novel Field-Programmable-Gate-Array (FPGA) implementation framework based on Lagrange programming neural network (LPNN), projection neural network (PNN) and proximal projection neural network (PPNN) is proposed which can be used to solve smooth and nonsmooth optimization problems. Firstly, Count Unit (CU) and Calculate Unit (CaU) a...

This paper proposes a finite-time converging proximal dynamic model (FPD) to deal with equilibrium problems. A distinctive feature of the FPD is its fast and finite-time convergence, in contrast to conventional proximal dynamic methods. It is shown that the solution of the proposed FPD converges to the solution of the corresponding equilibrium prob...

Since the depth information of images facilitates the analysis of the spatial distance of objects in computer vision applications, it is necessary to protect the image depth information. Thus this article proposes a novel red-green-blue-depth (RGB-D) image protection algorithm, which is implemented with the finite-time synchronization (FTS) of neur...

Sparse representation acts as a fundamental data science methodology for solving a wide range of problems in machine learning and engineering. In this paper, we respectively propose novel distributed continuous-time and discrete-time projection neurodynamic approaches for sparse recovery by seeking the minimum l <sub xmlns:mml="http://www.w3.org/...

In this paper, the bipartite synchronization of signed Lur’e network is studied under intermittent control, where the communication relationship of these adjacent nodes in the network can be either cooperative or competitive. Assuming that the network is structurally balanced, bipartite synchronization can be reached with some conditions and coordi...

During the past two years, a highly infectious virus known as COVID-19 has been damaging and harming the health of people all over the world. Simultaneously, the number of patients is rising in various countries, with many new cases appearing daily, posing a significant challenge to hospital medical staff. It is necessary to improve the efficiency...

The current investigation explores the leader-following consensus problem for nonlinear multiagent systems under the output feedback control mechanism and the event-triggered communication mechanism. Owing to the physical instrument constraints, a significant portion of the state variables is not readily available. Therefore, this article put forwa...

This paper develops two neurodynamic approaches for solving the L1-minimization problem with the linear inequality constraints. First, a centralized neurodynamic approach is proposed based on projection operator and nonnegative quadrant. The stability and global convergence of the centralized neurodynamic approach are analyzed by the Lyapunov metho...

The hybrid algorithm strategy proposed in this paper aims to combine the optimal power flow with voltage-var optimization to meet the load demand, reduce the transmission line losses and maintain the voltage within a practicable range. A distributed neural network algorithm is used to seek an optimal solution of active power flow which minimizes th...

This brief considers a distributed algorithm for solving ${L_{1}}$ -minimization problem based on nonlinear neurodynamic system. Compared with centralized algorithms, distributed algorithms have great potential in data privacy protection, distributed storage and processing of data. In this brief, ${L_{1}}$ -minimization problem is transformed i...

In this paper, a distributed nonsmooth nonconvex optimization (DNNO) problem with affine inequality and nonsmooth convex inequality constraints is studied. A continuous-time distributed neurodynamic algorithm is proposed to solve this problem. Under the assumed conditions, for any initial state, the solution of distributed neurodynamic algorithm is...

In this paper, a smoothing inertial neural network (SINN) is proposed for the Lp-L1(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 Lp-L1...

This letter develops a novel fixed-time stable neurodynamic flow (FTSNF) implemented in a dynamical system for solving the nonconvex, nonsmooth model L1-β2, β∈[0,1] to recover a sparse signal. FTSNF is composed of many neuron-like elements running in parallel. It is very efficient and has provable fixed-time convergence. First, a closed-form soluti...

In this paper, several recurrent neural networks (RNNs) for solving the L1-minimization problem are proposed. First, a one-layer RNN based on the hyperbolic tangent function and the projection matrix is designed. In addition, the stability and global convergence of the previously presented RNN are proved by the Lyapunov method. Then, the sliding mo...

This paper investigates two accelerated primal-dual mirror dynamical approaches for smooth and nonsmooth convex optimization problems with affine and closed, convex set constraints. In the smooth case, an accelerated primal-dual mirror dynamical approach (APDMD) based on accelerated mirror descent and primal-dual framework is proposed and accelerat...

This paper constructs an optimal scheduling model for combined heat and power generation units with heat storage and wind power generation considering carbon transaction costs and optimizes the output of each unit to reduce wind curtailment rate, carbon emissions, and total operating costs. In the case of considering transmission loss, the optimal...

This paper presents an approach for fixed‐time synchronization (FIXTS) of neural networks (NNs) by designing quantized intermittent controller. Under the intermittent controller, the synchronization between neural network systems with time delay can be realized. Based on intermittent strategy, FIXTs theory is proposed, and a sufficient condition is...

In this article, a distributed time‐varying convex optimization problem for multi‐agent systems is studied. The goal of this article is to use only the local information and interaction information of each agent to drive all agents to achieve consensus and minimize the time‐varying global objective function within a fixed time. In order to solve th...

Consider that the constrained convex optimization problems have emerged in a variety of scientific and engineering applications that often require efficient and fast solutions to optimization problems. Inspired by the Nesterov’s accelerated method for solving unconstrained convex and strongly convex optimization problems, in this paper we propose t...

In this study, we propose a modified projection neural network (PNN) with fixed-time convergence to solve the nonlinear projection equations. Under the assumptions of Lipschitz continuity and strict monotonicity, the existence of the solution and the stability in the Lyapunov sense of the proposed modified PNN are proved, which guarantee the conver...

This article proposes a novel fixed-time converging proximal neurodynamic network (FXPNN) via a proximal operator to deal with equilibrium problems (EPs). A distinctive feature of the proposed FXPNN is its better transient performance in comparison to most existing proximal neurodynamic networks. It is shown that the FXPNN converges to the solution...

This paper proposes a proximal neurodynamic network (PNDN) for solving mixed variational inequalities based on the proximal operator. It is shown that the proposed PNDN is globally exponentially stable under some mild conditions, and a stopping condition is provided for the PNDN. Furthermore, the proposed PNDN is applied in solving variational ineq...

In this paper, a competitive energy scheduling strategy game of N-microgrids (MGs) inside a distributed network is considered. Each microgrid (MG) aims to maximize its profit under the noncooperative game frame. The strategy-making of each MG depends on its equipment constraints, the aggregate energy supplies of all MGs, and the energy balance of s...

This article considers constrained nonsmooth generalized convex and strongly convex optimization problems. For such problems, two novel distributed smoothing projection neurodynamic approaches (DSPNAs) are proposed to seek their optimal solutions with faster convergence rates in a distributed manner. First, we equivalently transform the original co...

Chaotic maps with higher chaotic complexity are urgently needed in many application scenarios. This paper proposes a chaotification model based on sine and cosecant functions (CMSC) to improve the dynamic properties of existing chaotic maps. CMSC can generate a new map with higher chaotic complexity by using the existing one-dimensional (1D) chaoti...

In this paper, a novel projection neurodynamic network with fixed-time convergence is proposed for solving absolute value equations. In contrast to most existing projection neurodynamic networks, a conservative convergence time of the proposed projection neurodynamic network is presented. It is shown that the solution of the proposed approach conve...

This paper mainly focuses on the stability analysis and sampled data controller synthesis for nonlinear system represented by the Takagi–Sugeno (T‐S) fuzzy model. To avoid network congestion and save control resource, the event‐triggered communication strategy is applied. A novel fuzzy time‐dependent Lyapunov–Krosovskii functional (LKF) is proposed...

This article proposes a novel fixed-time converging forward-backward-forward neurodynamic network (FXFNN) to deal with mixed variational inequalities (MVIs). A distinctive feature of the FXFNN is its fast and fixed-time convergence, in contrast to conventional forward-backward-forward neurodynamic network and projected neurodynamic network. It is s...

In this paper, the dynamic economic dispatch problem (DEDP) of a hybrid power network is studied. The micro-grid model is constructed with three types of non-renewable power sources, renewable energy and energy storage batteries. Meanwhile, the pollutant emission costs and benefit function are considered here to maximize the total welfare. Firstly,...

A fully distributed microgrid system model is presented in this paper. In the user side, two types of load and plug-in electric vehicles are considered to schedule energy for more benefits. The charging and discharging states of the electric vehicles are represented by the zero-one variables with more flexibility. To solve the nonconvex optimizatio...

In this paper, a distributed time-varying convex optimization problem with inequality constraints is discussed based on neurodynamic system. The goal is to minimize the sum of agents’ local time-varying objective functions subject to some time-varying inequality constraints, each of which is known only to an individual agent. Here, the optimal solu...

This paper concentrates on the output feedback control problem for a class of nonlinear multiagent systems governed by the high-order strict-feedback model with time delay. Within the dynamic gain technique and the Lyapunov-like method, the dynamic gain state observer for each agent is put forward with the hope to compensate the impact induced by t...

In this article, a new projection neural network (PNN) for solving ${L_{\mathrm{1}}}$ -minimization problem is proposed, which is based on classic PNN and sliding mode control technique. Furthermore, the proposed network can be used to make sparse signal reconstruction and image reconstruction. First, a sign function is introduced into the PNN mo...

This article develops several centralized and collective neurodynamic approaches for sparse signal reconstruction by solving the $L_{1}$ -minimization problem. First, two centralized neurodynamic approaches are designed based on the augmented Lagrange method and the Lagrange method with derivative feedback and projection operator. Then, the optim...

In addition to economic goals, environmental constraints play an increasingly important role in the operation of multiple energy systems. A optimization model combining the minimization of energy cost and the carbon emissions is established to evaluate environmental impact, which is significant in the policy making policy making of the energy savin...

This paper proposes a novel proximal projection neural network (PPNN) to deal with mixed variational inequalities. It is shown that the PPNN has a unique continuous solution under the condition of Lipschitz continuity and that the trajectories of the PPNN converge to the unique equilibrium solution exponentially under some mild conditions. In addit...

In this paper, a novel distributed framework is proposed to solve the multi-objective problem which composed of several conflicting objective functions. In order to overcome the impact of inconsistent units of measurement for each objective function, a normalized function is applied to eliminate it. It will split a multi-objective optimization prob...

In this paper, we propose a smoothing inertial neurodynamic approach (SINA) which is used to deal with Lp-norm minimization problem to reconstruct sparse signals. Note that the considered optimization problem is nonsmooth, nonconvex and non-Lipschitz. First, the problem is transformed into a smooth optimization problem based on smoothing approximat...

This paper proposes a proximal neurodynamic model (PNDM) for solving inverse mixed variational inequalities (IMVIs) based on the proximal operator. It is shown that the PNDM has a unique continuous solution under the condition of Lipschitz continuity (L-continuity). It is also shown that the equilibrium point of the proposed PNDM is asymptotically...

In this paper, an economic emission dispatch (EED) model is developed to reduce fuel cost and environmental pollution emissions. Considering the development of new energy sources in recent years, the EED problem involves thermal units with the valve point effect and WTs. Meanwhile, it complies with demand constraint and generator capacity constrain...

In this paper, a neurodynamic algorithm with finite-time convergence to solve \({L_{\mathrm{{1}}}}\)-minimization problem is proposed for sparse signal reconstruction which is based on projection neural network (PNN). Compared with the existing PNN, the proposed algorithm is combined with the sliding mode technique in control theory. Under certain...

Threshold method is an important image segmentation method, which has been widely used in image segmentation. For this method, it is very important to choose a good threshold. The traditional threshold segmentation algorithm is implemented by exhaustive method, which makes the solution efficiency very low. This paper presents a collective neurodyna...

To improve the chaos complexity of existing chaotic maps, the fractional difference form of sine chaotification model (FSCM) is proposed in this paper based on discrete fractional calculus. In order to show its effect, we apply it to three chaotic maps. And the bifurcation diagrams and Lyapunov exponent of the generated new map are studied numerica...

This paper presents a microgrid system model considering three types of load and the user’s satisfaction function. The objective function with mixed zero-one programming is used to maximize every user’s profit and satisfaction in the way of the demand response management under real-time price. An energy function is used to transform the constrained...

This paper proposes a nonconvex mixed-integer programming(MIP) electricity model of household load that is classified into base load and plug-in electric vehicle (PEV). The main objective of this model is to minimize the electricity cost of domestic user by adjusting the charging and discharging strategy of PEV according to the information of real-...

A novel inertial projection neural network (IPNN) is proposed for solving inverse variational inequalities (IVIs) in this paper. It is shown that the IPNN has a unique equilibrium solution under the condition of Lipschitz continuity and that the solution trajectories of the IPNN converge to the equilibrium solution asymptotically if the correspondi...

In this paper, a plug-in hybrid electric vehicles energy consumption system is studied. In order to protect each player’s privacy, the information exchange is going on the neighboring players, and a connected undirected graph is used to pattern the information flow between the players. Hence, it is impossible for each player to access the aggregate...

Cloud capability is considered to be extended to the edge of the Internet for improving the security of data transmission. Compressive sensing (CS) has been widely studied as a built-in privacy-preserving layer to provide some cryptographic features while sampling and compressing, including data confidentiality guarantees and data integrity guarant...

This paper investigates a smoothing neural network (SNN) to solve a robust sparse signal reconstruction in compressed sensing (CS), where the objective function is nonsmooth l1-norm and the feasible set satisfies an inequality of lp-norm 2≥p≥1 which is used for measuring residual errors. With a smoothing approximate technique, the non-smooth and no...

This paper presents two methods for nonnegative matrix factorization based on an inertial projection neural network (IPNN). The first method applies two IPNNs for optimizing one matrix, with the other fixed alternatively, while the second optimizes two matrices simultaneously using a single IPNN. With the proposed methods, different local optimum s...

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...

This paper proposes a more reasonable objective function for combined economic emission dispatch problem. To solve it, Lagrange programming neural network (LPNN) is utilized to obtain optimal scheduling of a hybrid microgrid, which includes power generation resources, variable demands and energy storage system for energy storing and supplying. Comb...

This paper focuses on a multi-objective power management problem considering demand response in micro grid. The multi-objective problem consists of four conflicting objective functions: the average efficiency function of DG (Diesel Generation) unit, the emission of micro-grid, the dissatisfaction caused by demand response and the total profit funct...

In this paper, we consider a microgrid framework consisting of four power generation units, such as gas turbine, fuel cell, diesel generator and photovoltaic power generation. We focus on the minimum power generation cost under the lowest environmental pollution, combining with particle swarm optimization (PSO) and projection neural network. In thi...

This paper considers the cost-driven optimal energy management strategy under a complex environment, multimicrogrids system. To provide the flexibility of load in depth, the heating, ventilating, and air conditioning (HVAC) load is investigated and explicitly formulated incorporated with indoor temperature dynamic function. The operation cost of wi...

Discrete Fourier transform (DFT), inverse discrete Fourier transform (IDFT), and circular convolution are important tools for analyzing and designing discrete signals and systems, and are widely used in various industries. In order to pursue faster operational efficiencies or more accurate operational results, engineering calculations are often req...

In a (t, n)-threshold secret image sharing (SIS)scheme, the secrecy of a secret image relies on check and balance among participants. Unfortunately, the decentralized management also brings an underlying security flaw, i.e., once an attacker impersonates an authorized participant to submit a fake shadow for reconstruction no information about the s...

This article proposes a novel distributed approach to solve a new dynamic economic dispatch problem (DEDP) in which environmental cost function and ramp rate constraints are taken into consideration in islanded microgrid. In our proposed optimization model, the environmental cost function with E-exponential term and ramp rate constraints are consid...

In this paper, an innovative electric vehicle (EV) charging scheme is proposed, which is the first time to take into full account three charging modes in one framework, i.e. slow charging, quick charging and battery swapping. The aim is to minimize the charging cost to achieve valley filling effect. Furthermore, in order to address the proposed cha...

Distributed system is efficient and scalable in modern power grid. However, a distributed system is so susceptible to attack. In this paper, we formulate consensus-based distributed economic problem under attacks, and the convergence of the algorithm under a realizable and general stealthy attack is proved. Then the expression of the convergence po...

Smart grid and smart metering technologies are transforming the utility industry and the customer experience in search of a new energy deal that supports a more collaborative, eco-friendly, stable, reliable and cost-efficient system as a whole. In order to unlock the full benefits, utilities need now to develop new technologies like distributed opt...

In this paper, we design multi-energy management strategy for power-supply participants, heat-supply participants and consumers in a microgrid. The objectives of the management strategy are to maximize the social welfare and to balance the energy supply and demands. With the transmission loss, the proposed social welfare model, subject to practical...

The property of input-to-state stability (ISS) of inertial memristor-based neural networks with impulsive effects is studied. Firstly, according to the characteristics of memristor and inertial neural networks, the inertial memristor-based neural networks are built. Secondly, based on the impulsive control theory, the average impulsive interval app...