# Lin XiaoHunan Normal University

Lin Xiao

Professor

## About

221

Publications

15,513

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Introduction

Lin Xiao received the Ph.D. degree from Sun Yat-sen University, Guangzhou, China, in 2014. He is currently a Professor with the College of Information Science and Engineering, Hunan Normal University, Changsha, China. He has authored over 100 papers in international conferences and journals, such as the IEEE-TNNLS, the IEEE-TCYB, the IEEE-TII and the IEEE-TSMCA. His main research interests include neural networks, robotics, and intelligent information processing.

**Skills and Expertise**

Additional affiliations

September 2009 - June 2014

## Publications

Publications (221)

Taking advantage of the burgeoning zeroing neural network (ZNN) and the widely used fuzzy logic system (FLS), a novel double integral noise-tolerant fuzzy ZNN (DINTFZNN) model for solving the time-varying Sylvester matrix equation (TVSME) is proposed in this paper. The special feature of the DINTFZNN model lies in the adoption of a double integral...

This article investigates a leader-following bipartite consensus issue for uncertain nonlinear heterogeneous multiagent systems (MASs). Initially, within the framework of optimal control theory, we employ the reinforcement learning (RL) algorithm to derive an approximate solution to the Hamilton-Jacobi-Bellman equation (HJBE). Specifically, the neu...

Considering the extensive research on zeroing neurodynamic (ZN), a self-adaptive and enhanced fixed-time convergent zeroing neurodynamic (SEFC-ZN) method for addressing time-variant problems is presented in this paper based on a discrete fuzzy matrix (DFM) design parameter and a novel advanced sign-bi-power activation function (NASbpAf). Due to the...

With high wind speed, low turbulence, and high output, offshore wind power has gradually become a new area of wind power development. Nevertheless, the chaos phenomenon of offshore wind turbines manifests in some severe environments and the entire power generation system is affected. A variety of projective synchronization schemes are proposed to c...

The coupling of multivariate repeated systems and the nonlinearity that is difficult to characterize through mechanisms, along with actuator constraints and data noise pollution, pose challenges in achieving precise tracking tasks. To address these issues, a novel data-driven robust iterative learning predictive control (ILPC) scheme is proposed. T...

The zeroing neural network (ZNN) has been utilized in various control applications, such as tracking and motion control. While ZNN has been widely employed, its utilization in consensus control schemes is rarely reported. In this study, we propose a novel distributed fixed-time ZNN (DFTZNN) scheme designed to achieve fixed-time and robust consensus...

Image fusion can obtain the superior information and reduce the noise in the source image by designing a specific scheme. However, the noise in image fusion has been a difficult issue and hard to handle. In this article, a variable-gain fixed-time convergent and robust zeroing neural network (VFCR-ZNN) model is proposed to figure out the image fusi...

In this article, a low-order zeroing neural network (LZNN), a high-order ZNN (HZNN), and a variable-parameter ZNN (VZNN) are designed and applied to the time-changing Cholesky decomposition of any positive-definite matrix, where the LZNN and HZNN models are generated based on the traditional and high-order evolutionary formulas, respectively. In ad...

Currently, there is a dearth of algorithms for solving the dynamic quaternion least squares problem (DQLSP), and the traditional numerical methods cannot solve dynamic problems effectively. To solve the DQLSP, a predefined-time noise immunity ZNN (PTNIZNN) model and a novel activation function are presented, building upon the traditional zeroing ne...

Time-varying linear equations (TVLEs) play a fundamental role in the engineering field and are of great practical value. Existing methods for the TVLE still have issues with long computation time and insufficient noise resistance. Zeroing neural network (ZNN) with parallel distribution and interference tolerance traits can mitigate these deficienci...

This article proposes predefined-time adaptive neural network (PTANN) and event-triggered PTANN (ET-PTANN) models to efficiently compute the time-varying tensor Moore–Penrose (MP) inverse. The PTANN model incorporates a novel adaptive parameter and activation function, enabling it to achieve strongly predefined-time convergence. Unlike traditional...

Due to the excellent time-varying problem-solving capability of zeroing neural network (ZNN), many redundancy resolution schemes based on ZNN have been proposed for robots. The work proposes a fixed-time robust ZNN (FTRZNN) model with adaptive parameters to effectively address redundancy resolution problems of robots in the presence of noises. Diff...

Sliding mode control (SMC) is widely recognized as an effective control scheme for the synchronization of chaotic systems (CSs). However, numerous existing SMC schemes for chaos synchronization assume a noise-free environment and depend on multiple parameters. In this study, to address these limitations, two zeroing neural network (ZNN) schemes are...

To overcome the disadvantages of the current zeroing neural network (ZNN) in noise tolerance, this article first proposes an intensive noise-tolerant ZNN (INT-ZNN) by introducing a novel fuzzy control approach (FCA). This FCA is designed dexterously according to the variation of two errors related to the INT-ZNN. Thus, the most feature of the INT-Z...

Square root finding plays an important role in many scientific and engineering fields, such as optimization, signal processing and state estimation, but existing research mainly focuses on solving the time-invariant matrix square root problem. So far, few researchers have studied the time-varying tensor square root (TVTSR) problem. In this study, a...

Time-varying matrix inversion (TVMI) is a basic mathematical problem, which is widely involved in many scientific fields. In this paper, an event-triggered control fuzzy adaptive zeroing neural network (ETC-FAZNN) model is proposed for solving the TVMI problem, where the fuzzy adaptive convergence parameter (FACP) is got by the redesigned fuzzy log...

A dynamic gain fixed-time (FXT) robust zeroing neural network (DFTRZNN) model is proposed to effectively solve time-variant equality constrained quaternion least squares problem (TV-EQLS). The proposed approach surmounts the shortcomings of conventional numerical algorithms which fail to address time-variant problems. The DFTRZNN model is construct...

As a systematic approach, zeroing neural network (ZNN) is an elegant tool in control applications. However, the application of ZNNs in multi-agent systems still needs further research. Adaptive control schemes with adjustable convergence speed are important in practical application, but researchers mainly use explicit and direct rules to update the...

Quadratic programming with equality constraint (QPEC) problems have extensive applicability in many industries as a versatile nonlinear programming modeling tool. However, noise interference is inevitable when solving QPEC problems in complex environments, so research on noise interference suppression or elimination methods is of great interest. Th...

Time-varying complex-valued tensor inverse (TVCTI) is a public problem worthy of being studied, while numerical solutions for the TVCTI are not effective enough. This work aims to find the accurate solution to the TVCTI using zeroing neural network (ZNN), which is an effective tool in terms of solving time-varying problems and is improved in this a...

Generalized projective synchronization (GPS) as a deeply influential chaos synchronization has always attracted lots of attention. However, plenty of traditional control methods do not predict its synchronization time or have no regard for the interference of noise in practical applications. Inspired by the fact that zeroing neural network (ZNN) ca...

This paper presents a dynamic model based on neutrosophic numbers and a neutrosophic logic engine. The introduced neutrosophic logic/fuzzy adaptive Zeroing Neural Network dynamic is termed NSFZNN and represents an improvement over the traditional Zeroing Neural Network (ZNN) design. The model aims to calculate the matrix pseudo-inverse and the mini...

Presently, numerical algorithms for solving quaternion least-squares problems have been intensively studied and utilized in various disciplines. However, they are unsuitable for solving the corresponding time-variant problems, and thus few studies have explored the solution to the time-variant inequality-constrained quaternion matrix least-squares...

As an extension of the Lyapunov equation, the time-varying plural Lyapunov tensor equation (TV-PLTE) can carry multidimensional data, which can be solved by zeroing neural network (ZNN) models effectively. However, existing ZNN models only focus on time-varying equations in field of real number. Besides, the upper bound of the settling time depends...

Zeroing neural network (ZNN) can effectively solve the matrix flows inversion problem. Nevertheless, quite a few related research works focus on the improvement of the convergence and robustness performance of the ZNN models and ignore the conservatism of their predefined time. Therefore, this article adopts a polymorphous activation function (PAF)...

As a common and significant problem in the field of industrial information, the time-varying quaternion matrix equation (TV-QME) is considered in this article and addressed by an improved zeroing neural network (ZNN) method based on the real representation of the quaternion. In the light of an improved dynamic parameter (IDP) and an innovative acti...

Zeroing neural network (ZNN) is an effective means of handling the dynamic Lyapunov equation. However, the conventional ZNN’s convergence speed relies heavily on its initial value, and it is incapable of tolerating some large time-varying noises. Therefore, a complex-valued variable-parameter predefined-time convergence ZNN (CVPZNN) is proposed for...

On account of the rapid progress of zeroing neural network (ZNN) and the extensive use of fuzzy logic system (FLS), this article proposes an intelligent fuzzy robustness ZNN (IFR‐ZNN) model and applies it to solving the time‐variant Stein matrix equation (TVSME) problem. Be different from ZNN models before, the IFR‐ZNN model uses a fuzzy parameter...

In this chapter, a superior design formula activated by noise‐tolerant nonlinear functions is proposed to achieve the denoising and finite‐time convergence of zeroing neural network (ZNN). According to this design formula, a robust finite‐time zeroing neural network (R‐FTZNN) is developed and applied to robotic motion tracking illustrated via time‐...

In this chapter, a finite‐time zeroing neural network (FTZNN) model is proposed and studied for finding the matrix square root. The FTZNN model fully utilizes a nonlinearly activated sign‐bi‐power function, and thus possesses faster convergence ability. The upper bound of convergence time of the FTZNN model is theoretically derived and estimated by...

In this chapter, a robust finite‐time zeroing neural network (R‐FTZNN) is devised and presented to solve time‐dependent nonlinear minimization under various external disturbances. The proposed R‐FTZNN model simultaneously possesses two characteristics, i.e. finite‐time and noise suppression. Besides, rigorous theoretical analyses are given to prove...

In this chapter, we propose a novel integral design scheme for finding the robust solution of time‐varying matrix inequalities. The core idea of this method is to add an integral term in the construction of the error function to make the model have error memory, so as to eliminate static difference. Meanwhile, appropriate activation functions (AFs)...

In this chapter, by suggesting a new nonlinear activation function, a robust and fixed‐time zeroing neural network (R‐FTZNN) model is proposed and analyzed for time‐variant nonlinear equation (TVNE). The R‐FTZNN model not only converges to the theoretical solution of TVNE within a fixed time (a kind of finite‐time convergence), but also rejects ext...

In this chapter, online solution to time‐varying matrix inverse is investigated by proposed a finite‐time zeroing neural network (FTZNN) model, which is evolved by a new design formula. The purpose of presenting the novel design formula is to accelerate the convergence of the FTZNN model to further achieve the finite‐time convergence. Theoretical a...

In this chapter, a finite‐time zeroing neural network (FTZNN) model, together with a specially‐constructed activation function, is proposed and investigated for finding the root of nonlinear equation. The investigated FTZNN model in the form of implicit dynamics has the following advantages: (i) has better consistency with actual situations; (ii) h...

In this chapter, we present a new design formula and apply it to zeroing neural network (ZNN). Further, a finite‐time zeroing neural network (FTZNN) model is proposed and investigated for finding the time‐varying matrix square root. Theoretical analyses of the novel design formula and the FTZNN model are proposed in detail. Besides, simulative expe...

In this chapter, we propose a unified finite‐time zeroing neural network (U‐FTZNN) model for solving time‐varying quadratic programming (QP) problems subject to equality or inequality constraints. The proposed U‐FTZNN model mainly has advantages in the following three aspects: (i) solving QP problems with or without inequality constraints in a unif...

In this chapter, two noise‐tolerant predefined‐time zeroing neural network (NT‐PTZNN) models are established by devising two novelly constructed nonlinear activation functions (AFs) to find the accurate solution of the time‐variant Sylvester equation in the presence of various noises. The proposed two NT‐PTZNN models are activated by two novel AFs,...

In this chapter, based on a new evolution formula, a novel finite‐time zeroing neural network (FTZNN) is proposed and studied for solving a nonstationary Lyapunov equation. The convergence performance has a remarkable improvement for the proposed FTZNN model and can be accelerated to finite time. Besides, by solving the differential inequality, the...

In this chapter, a finite‐time zeroing neural network (FTZNN) is proposed and investigated for solving online Lyapunov equation. The proposed FTZNN model adopts a sign‐bi‐power activation function, and thus possesses the best convergence performance. Furthermore, we prove that the FTZNN model can converge to the theoretical solution of Lyapunov equ...

In this chapter, a new noise tolerant finite‐time zeroing neural network (NT‐FTZNN) model using a versatile activation function (VAF) is presented and introduced for solving time‐dependent matrix inversion (TVMI) problem. The convergence and robustness of the NT‐FTZNN model are mathematically analyzed in detail. Two comparative numerical simulation...

In this chapter, we present a systematic and constructive procedure on using zeroing neural network (ZNN) to design control laws based on the efficient solution of dynamic Lyapunov equation. We particularly address three important aspects in the design: (i) the global stability of ZNN, to guarantee the effectiveness of the solution; (ii) the robust...

In this chapter, a nonlinear finite‐time zeroing neural network (N‐FTZNN) model is proposed and studied for real‐time solution of the equality‐constrained quadratic optimization with nonstationary coefficients. The proposed N‐FTZNN model possesses the much superior convergence performance (i.e. finite‐time convergence). Furthermore, the upper bound...

In this chapter, to solve dynamic Sylvester equation in the presence of additive noises, a novel finite‐time zeroing neural network (N‐FTZNN) with finite‐time convergence and excellent robustness is proposed and analyzed. The proposed N‐FTZNN is based on an ingenious integral design formula activated by nonlinear functions, which are able to expedi...

In this chapter, three novel finite‐time zeroing neural network (FTZNN) models are designed and analyzed to solve time‐varying linear matrix inequalities (LMIs). To make the Matlab toolbox calculation processing more conveniently, the matrix vectorization technique is used to transform the matrix‐valued FTZNN models into the vector‐valued FTZNN mod...

This chapter designs two finite‐time zeroing neural network (FTZNN) models for time‐varying linear matrix equation through taking two new activation functions into consideration. Theoretical analysis proves that two new activation functions can not only accelerate the convergence rate of the FTZNN models but also come true the finite‐time convergen...

In this chapter, an improved varying parameter finite‐time zeroing neural network (IVP‐FTZNN) model is established and researched to solve the time‐varying matrix inversion (TVMI) problem. Specifically, the value of the proposed novel time‐varying parameter in the IVP‐FTZNN model can grow rapidly over time, which can better meet the needs of ZNN in...

In this chapter, we propose a nonlinearly activated finite‐time zeroing neural network (FTZNN) model to solve time‐varying nonlinear equations in real time. In the theory part, the upper bound of convergence time is estimated analytically. Simulations are performed to evaluate the performance of the proposed FTZNN model, which substantiates the eff...

In this chapter, for obtaining better convergence performance when zeroing neural network (ZNN) is applied to solve time‐varying Sylvester equation (TVSE), three different types of adaptive design coefficients for the sign‐bi‐power activation function are developed and investigated. Based on these adaptive coefficients, three new adaptive finite‐ti...

In this article, a novel distributed gradient neural network (DGNN) with predefined-time convergence (PTC) is proposed to solve consensus problems widely existing in multiagent systems (MASs). Compared with previous gradient neural networks (GNNs) for optimization and computation, the proposed DGNN model works in a nonfully connected way, in which...

Linear equations (LEs) play an essential role in mathematics. Nevertheless, the vast majority of fixed-parameter zeroing neural network (ZNN) models are not fast enough to solve LEs. So, a novel parameter-changing ZNN (PCZNN) is proposed and studied in the quest for resolving LEs more efficiently. In contrast to the conventional ZNN and finite-time...

Since the solution of time-variant nonlinear inequality systems is trapped by the convergence performance of the models, this paper explores an enhanced nonlinear sign-bi-power activation function (AF) and further obtains a zeroing neural network (ZNN) model for solving time-variant nonlinear inequality systems, which is called nonlinear activated...

This article aims to studying how to solve dynamic Sylvester quaternion matrix equation (DSQME) using the neural dynamic method. In order to solve the DSQME, the complex representation method is first adopted to derive the equivalent dynamic Sylvester complex matrix equation (DSCME) from the DSQME. It is proven that the solution to the DSCME is the...

Based on the standard method of zeroing neural network (ZNN) for time-varying matrix problems, three improved ZNN models are proposed and extended to solve the time-varying Sylvester tensor equation (TV-STE) in finite-time. These presented ZNN models, which mainly use classical sign-bi-power (S-B-P) activation function and varying parameters, are S...

External disturbances are always inevitable in complex application scenarios, especially in synchronizing chaotic systems. This paper proposes a noise-restraint zeroing neural network (NRZNN) model to expedite the synchronisation of chaotic systems under external disturbances. Its associative controller is then evolved to suppress the interference...

Zeroing neural network (ZNN) has a wide application in various fields, which is a very important and novel type of recurrent neural network (RNN). To deepen and expand the design mechanism of the traditional zeroing neural network (TZNN), this paper proposes a new type of the ZNN model with a fuzzy adaptive parameter to settle the time-variant line...

Stuck in the speed and dimensionality of settling time-variant linear matrix inequality (LMI), this paper for the first time proposes two finite-time variable parameter zeroing neural network (FTVPZNN) models to settle the time-variant LMI. The first model is called the FTVPZNN-C model activated by the conventional sign-bi-power (S-B-P) function. T...

In order to solve the time-varying quadratic programming (TVQP) problem more effectively, a new self-adaptive zeroing neural network (ZNN) is designed and analyzed in this article by using the Takagi-Sugeno fuzzy logic system (TSFLS) and thus called the Takagi-Sugeno (T-S) fuzzy ZNN (TSFZNN). Specifically, a multiple-input-single-output TSFLS is de...

Two nonlinear zeroing neural network (ZNN) models with prescribed-time convergence for time-dependent matrix LR and QR factorization are proposed in this paper. To do so, two algorithms and two error functions are constructed to transform the time-dependent matrix LR and QR factorization problems into time-dependent linear equation systems, respect...

Matrix inversion is commonly encountered in the field of mathematics. Therefore, many methods, including zeroing neural network (ZNN), are proposed to solve matrix inversion. Despite conventional fixed-parameter ZNN (FPZNN), which can successfully address the matrix inversion problem, it may focus on either convergence speed or robustness. So, to s...

Aiming at the efficient online solution of the time-variant linear matrix inequality (LMI) under non-ideal conditions (e.g., noise pollution), a predefined-time convergent and integral-enhanced zeroing neural network (PCIE-ZNN) model is built for the first time in this work. Compared with existing zeroing neural network (ZNN) models for settling th...

Time-varying tensor inversion (TVTI) problem is a kind of general time-varying inversion problem in mathematics because scalars, vectors and matrices can all be represented by tensors. The TVTI problem is based on a novel tensor product [termed the TensorFlow (TF) product], which is extracted from the TensorFlow. For solving such a prevalent proble...

Based on practical applications of the complex-valued Sylvester equation and the effectiveness of zeroing neural network (ZNN) in solving time-varying problems, two discrete nonlinear and noise-tolerant ZNN (DNN-TZNN) models are proposed to solve the time-varying augmented complex Sylvester (TACS) equation by using the Adams-Bashforth formula to di...

Zeroing neural network (ZNN), an effective method for tracking solutions of dynamic equations, has been developed and improved by various strategies, typically the application of nonlinear activation functions (AFs) and varying parameters (VPs). Unlike VPs, AFs applied in ZNN models act directly on real-time error. The processing unit of v needs to...

A correlation between fuzzy logic systems (FLS) and zeroing neural networks (ZNN) design is investigated. It is shown that the gain parameter included in ZNN design can be dynamically adjusted over time by means of an appropriate value derived as the output of a properly defined FLS which includes appropriately defined membership functions and fuzz...

Zeroing neural network (ZNN) is an effective neural solution to time-varying problems including time-varying complex Sylvester equations. Generally, a ZNN model involves a convergence design parameter (CDP) that influences its convergence rate. In traditional fixed-parameter ZNNs (FP-ZNNs), the CDPs are set to be constant, which is not realistic si...

As a category of the recurrent neural network (RNN), zeroing neural network (ZNN) can effectively handle time-variant optimization issues. Compared with the fixed-parameter ZNN that needs to be adjusted frequently to achieve good performance, the conventional variable-parameter ZNN (VPZNN) does not require frequent adjustment, but its variable para...

In order to improve the effect of the ExpSign activation function (ESAF) and Sinh-Sign activation function (SSAF) on the convergence and robustness of the ZNN model, two fuzzy adaptive activation functions (AFs), named FAESAF and FASSAF, are constructed by using a Mamdani fuzzy logic controller (MFLC) in this paper. Thus, a novel zeroing neural net...

Motivated from the convergence capability achieved by gradient neural network (GNN) and zeroing neural network (ZNN) for matrix inversion, a novel hybrid GNN-ZNN (H-GNN-ZNN) model is proposed by introducing a fuzzy adaptive control strategy to generate a fuzzy adaptive factor that can change its size adaptively according to the residual error. Due...

For solving dynamic generalized Lyapunov equation, two robust finite-time zeroing neural network (RFTZNN) models with stationary and nonstationary parameters are generated through the usage of an improved sign-bi-power (SBP) activation function (AF). Taking differential errors and model implementation errors into account, two corresponding perturbe...

This paper for the first time extends the zeroing neural network (ZNN) method to address the problem of dynamic quaternion-valued matrix inversion. Due to the noncommutative property of the quaternion multiplication, the complex representation method is first adopted to transform quaternion-valued matrices into the corresponding complex-valued matr...

In this paper, considering the effectiveness and efficiency in solving time-varying problems, a new zeroing neural network (ZNN) is proposed to solve time-varying linear equations with column full rank coefficient matrix. In addition, two novel nonlinear activation functions are developed to enhance the comprehensive performance of the ZNN model. I...

Based on extensive applications of the time-variant quadratic programming with equality and inequality constraints (TVQPEI) problem and the effectiveness of the zeroing neural network (ZNN) to address time-variant problems, this article proposes a novel finite-time ZNN (FT-ZNN) model with a combined activation function, aimed at providing a superio...

This paper devotes to solving time-varying linear matrix equations (TVLMEs) from the viewpoint of high-order neural networks. For this purpose, high-order zeroing neural network (ZNN) models are designed and studied to solve TVLMEs. Compared with the first-order ZNN model for TVLMEs, the proposed high-order ZNN models are based on the design of the...

A new type neural network model is proposed and studied to solve time- variant linear equations in this paper. Distinct from the ordinary zeroing neural network (ZNN), the activation function part of this neural network model adopts the sign-bi-power function, and the design parameter adopts a time-variant one of the piecewise type, so it is named...

In this article, two Adams–Bashforth-type integration-enhanced discrete-time zeroing neural dynamic (ADTIZD) models are proposed to solve the time-varying complex Sylvester equation (TVCSE) problem in the first time. In ADTIZD models, Adams–Bashforth discrete formulas as novel discrete formulas are used, giving our ADTIZD models higher accuracy [tr...

A noise-suppression variable-parameter zeroing neural network (NSVPZNN) is proposed to handle the dynamic Sylvester equation in this work. Distinct from the previous zeroing neural networks (ZNNs), a new nonlinear activation function and a specially-constructed time-variant parameter are developed to construct the novel NSVPZNN model. Therefore, th...

This paper proposes two complex-valued zeroing neural network (Cv-ZNN) models for solving dynamic complex time-variant linear equations. The models involve two complex-valued nonlinear processing methods and adapt two real-valued activation functions. The convergence and robustness of the two Cv-ZNN models are analyzed comprehensively. Firstly, the...

For solving complex-valued linear matrix equations with time-varying coefficients (CV-LME-TVC) problem in the complex field, this paper proposes a parameter-changing and complex-valued zeroing neural network (PC-CVZNN) model through integrating a new parameter-changing function. As compared to previous complex-valued zeroing neural networks (CVZNNs...

In accordance with the advantages of zeroing neural network (ZNN) with the parallel processing character and fuzzy logic systems for calculating the uncertainties, two complex-type fuzzy ZNN (CtFZNN) models, which are mainly derived from two different limit forms of the Drazin inverse, are developed for solving the time-dependent complex-value Draz...

Matrix inversion frequently occurs in the fields of science, engineering, and related fields. Numerous matrix inversion schemes are often based on the premise that the solution procedure is ideal and noise-free. However, external interference is generally ubiquitous and unavoidable in practice. Therefore, an integrated-enhanced zeroing neural netwo...

Robust synchronization of chaotic systems with time-varying external disturbances is a hot topic in the field of science and engineering. In view of the negative influence of complex noise on the synchronization of chaotic systems, a noise-suppression zeroing neural network (NSZNN) is designed and proposed to effectively resist time-varying externa...

This paper is primarily concerned with finite-time and predefined-time convergence design for a class of general Zeroing Neural Network (ZNN) by constructing different activation functions (AFs). Based on the limit comparison test for improper integrals, some useful theorical criteria are proposed to determine whether a nonlinear-activated ZNN mode...

The time-varying Lyapunov equation is an important problem that has been extensively employed in the engineering field and the Zeroing Neural Network (ZNN) is a powerful tool for solving such problem. However, unpredictable noises can potentially harm ZNN’s accuracy in practical situations. Thus, the comprehensive performance of the ZNN model requi...

By exploiting two simplified nonlinear activation functions, two zeroing neural network (ZNN) models are designed and studied to efficiently tackle the time-varying matrix pseudoinversion problem. Compared with ZNN activated by previously presented activation functions, these two simplified finite-time ZNN (SFTZNN) models (called SFTZNN1 and SFTZNN...

Solving linear inequalities is widely used to various fields, and it plays a more and more crucial role in practical engineering application. However, the existing recurrent neural network models to solve this problem only achieve global convergence without any noise. To overcome this disadvantage, in this paper, we propose a novel integral design...