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Finite-time synchronization (FTS) of uncertain fractional-order memristive neural networks (FMNNs) with leakage and discrete delays is studied in this paper, in which the impacts of uncertain parameters as well as external disturbances are considered. First, the fractional-order adaptive terminal sliding mode control scheme (FATSMC) is designed, which can effectively estimate the upper bounds of unknown external disturbances. Second, the FTS of the master–slave FMNNs is realized and the corresponding synchronization criteria and the explicit expression of the settling time (ST) are obtained. Finally, a numerical example and a secure communication application are provided to demonstrate the validity of the obtained results. [ABSTRACT FROM AUTHOR]

This paper considers the finite‐time synchronization problem of complex networks with hybrid‐coupled time‐varying delay. We combine aperiodically intermittent pinning control method and event‐triggered control method to design the controller, which can guarantee that the states of all nodes can realize synchronization in the finite‐time. The event‐triggered mechanism is introduced according to the difference of error values between nodes and reduces the communication frequency. Besides, the pinning nodes are reselected at each triggered moment by a given algorithm. Based on the appropriate inequality scaling technique and Lyapunov function, the finite‐time synchronization criteria are obtained for the given network system model. Finally, the feasibility of the theoretical results is proved through a simulation example. [ABSTRACT FROM AUTHOR]

This study mainly investigates finite‐time multi‐switching synchronisation of multiple uncertain complex chaotic systems with network transmission mode. Both the unknown parameters and disturbances are considered. By establishing the desired multi‐switching rules, the definition of finite‐time hybrid synchronisation for multi‐systems is first given. Then, sliding mode control approach is used to design the synchronisation controllers, which guarantee that any states of each drive system can synchronise the expected states of every response system in finite time. Simultaneously, the adaptive laws effectively estimate the unknown parameters and the unknown constants of disturbances. Finally, simulation example and analysis display the correctness and effectiveness of all innovations. [ABSTRACT FROM AUTHOR]

Two kinds of finite-time complex function synchronization for multiple complex-variable chaotic systems are investigated. Both the transmission mode and combination mode are respectively considered. By considering complex functions as the scaling factors, the definitions of the two different synchronization mechanisms are established, and the appropriate schemes are proposed to guarantee the finite-time stability of all error systems. Furthermore, two verifiable criteria are derived to synchronize multi-systems. Finally, detailed simulation analysis is provided to validate the effectiveness of all innovations. [ABSTRACT FROM AUTHOR]

This paper mainly investigates finite-time synchronization of multiple different-order chaotic systems. Two kinds of different network synchronization modes are considered here, and the definitions of finite-time synchronization errors are given for such systems by constructing the proper vectors mapping functions. On the basis of finite-time control idea, the synchronization schemes are developed to ensure the asymptotical stability of two classes of different error systems in finite-time. Afterward, two numerical examples are calculated and simulated to illustrate the effectiveness and feasibility of proposed strategies. [ABSTRACT FROM AUTHOR]

This paper mainly investigates two kinds of sliding mode synchronization (SMS) for multiple chaotic systems with unknown parameters and disturbances. Both multiple coupled systems and uncoupled systems are considered. For multiple uncoupled chaotic systems (MUCSs), the sliding mode control scheme is designed to ensure that multiple response systems synchronize with one drive system under the effects of external disturbances, and the appropriate adaptive laws are proposed to estimate unknown parameters. For multiple coupled chaotic systems (MCCSs), the definition of transmission synchronization is given first. Furthermore, a special integral sliding surfaces is selected, and the corresponding controllers with the compensation terms are developed to realize SMS between every drive chaotic system and every respond system in transmission mode. Finally, some numerical examples are presented to illustrate the validity of theoretical results. [ABSTRACT FROM AUTHOR]

This paper investigates two classes of synchronization problems of multiple chaotic systems with unknown uncertainties and disturbances by employing sliding mode control. Modified projective synchronization and transmission synchronization are discussed here. For the modified projective synchronization problem, sliding mode controllers are designed to ensure that multiple response systems synchronize with one drive system under the effects of external disturbances. For the transmission synchronization problem, based on adaptive sliding mode control, an integral sliding surface is selected and the adaptive laws are derived to tackle unknown uncertainties and disturbances for such systems. A class of nonlinear adaptive sliding mode controllers is developed to guarantee asymptotical stability of the error systems so that all chaotic systems can synchronize with each other. Simulation results are given to illustrate the effectiveness of the proposed schemes by comparing with the existing methods. [ABSTRACT FROM AUTHOR]

Logistics center location optimization is one of the core issues in the study of logistics networks. A sensible logistics center distribution can improve the efficiency of goods transportation and the operation efficiency of logistics enterprises. In this paper, we study the problem of optimizing the location of logistics distribution centers in logistics networks with the objective of minimizing the operating costs of logistics distribution centers. Then, we presents a distribution center location optimization model based on minimizing operating costs under uncertain demand with logistics node capacity scalability. Firstly, the future goods quantity prediction data of each node is obtained through the grey-residual Markov chain prediction model. Then, a logistics center location optimization model with three node expansion mechanisms is developed with the objective of minimizing the objective function. Finally, by using the forecast data of goods demand obtained by the grey-residual Markov chain prediction method, we conducted simulation experiments on the cost-minimizing logistics center location optimization model under three different node expansion methods. The corresponding simulation results are obtained by particle swarm optimization algorithm to prove the effectiveness of the model. • Forecast the quantity of goods received by logistics nodes in the future. • A logistics location optimization model based on node scalability is established. • Consider the constraint relationship between distance cost and inventory expensive. [ABSTRACT FROM AUTHOR]

This paper investigates the synchronization of multiple chaotic systems with unknown parameters using adaptive control method, and two kinds of different synchronization modes are considered here. One is that more response systems synchronize with one drive system, and the other is the ring transmission synchronization, which guarantees that all chaotic systems can synchronize with each other. The definition of adaptive synchronization of multiple chaotic systems with unknown parameters is given, and then based on the idea of adaptive control method, adaptive laws are derived to estimate the unknown parameters, and nonlinear adaptive controllers are developed to ensure the asymptotical stability of two classes of error systems. Finally, simulation results are presented to verify the effectiveness of proposed synchronization schemes. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the hybrid synchronization behavior in an array of coupled chaotic systems with ring connection, of which means complete synchronization (CS) and anti-synchronization (AS) could coexist. First, the anti-synchronization controllers are designed, which can transform the synchronization error dynamic system into a nonlinear system with an antisymmetric structure. Second, we investigate the complete synchronization behavior in such a chaotic system under the anti-synchronization control. After that, the stability conditions are given for reaching hybrid synchronization. Finally, numerical examples and simulation results are presented to verify and validate the hybrid synchronization behavior in coupled chaotic system. [ABSTRACT FROM AUTHOR]

• Considering only a rough boundary of the singularly perturbed parameter κ is available, the bounding ellipsoid obtained this paper is independent on κ such that this ellipsoid can comprise the reachable set of this system for any admissible singularly perturbed parameters. • A κ -dependent LKF is constructed by unequally dividing the delay into two sub-intervals to lead to more superior criteria for the reachable set estimation. • The conditions derived are all independent on the singularly perturbed parameter κ to avoid ill-conditioned numerical trouble. Reachable set estimation problem in allusion to continuous-time singularly perturbed systems that take on time-varying delays and bounded disturbances is discussed in this paper. In many singularly perturbed systems, since the perturbed parameter κ is not available, our task is to determine an ellipsoid as small as possible which can be independent on κ. In this case, for any admissible singularly perturbed parameters, this ellipsoid can surround the states of the system. First, in order to obtain more accurate result, the delay is unequally divided into two sub-intervals and a κ -dependent Lyapunov-Krasocskii functional is established. Then, with the aid of reciprocally convex inequality for each sub-interval, sufficient delay-dependent conditions are established which can make the system states contained by a κ -independent ellipsoid. Finally, a numerical example is given to verify the validity of the result. [ABSTRACT FROM AUTHOR]

Considering the media coverage and age‐dependent education, a deterministic and a stochastic class‐age‐structured rumor propagation models are studied, respectively. First, the deterministic rumor propagation model is characterized by a coupled system of ordinary and partial differential equations. The positivity and boundness of solutions are proved, and the basic reproduction number is derived. Second, the stochastic rumor propagation model is formulated by stochastic differential equations. The existence of global positive solutions in model is discussed with the Itô's formula and stochastic Lyapunov function. Additionally, by utilizing comparison principle of stochastic differential equations and the strong law of large numbers, several sufficient conditions for extinction and persistence of the rumor are derived. Finally, numerical simulations are carried out for illustrating the results. [ABSTRACT FROM AUTHOR]

With the rapid growth of the Internet, a wealth of movie resources are readily available on the major search engines. Still, it is unlikely that users will be able to find precisely the movies they are more interested in any time soon. Traditional recommendation algorithms, such as collaborative filtering recommendation algorithms only use the user's rating information of the movie, without using the attribute information of the user and the movie, which has the problem of inaccurate recommendations. In order to achieve personalized accurate movie recommendations, a movie recommendation algorithm based on a multi-feature attention mechanism with deep neural networks and convolutional neural networks is proposed. In order to make the predicted movie ratings more accurate, user attribute information and movie attribute information are added, user network and movie network are presented to learn user features and movie features, respectively, and a feature attention mechanism is proposed so that different parts contribute differently to movie ratings. Text features are also extracted using convolutional neural networks, in which an attention mechanism is added to make the extracted text features more accurate, and finally, personalized movie accurate recommendations are achieved. The experimental results verify the effectiveness of the algorithm. The user attribute features and movie attribute features have a good effect on the rating, the feature attention mechanism makes the features distinguish the degree of importance to the rating, and the convolutional neural network adding the attention mechanism makes the extracted text features more effective and achieves high accuracy in MSE, MAE, MAPE, R2, and RMSE indexes. [ABSTRACT FROM AUTHOR]

This paper considers the synchronization and anti-synchronization problem of N different coupled chaotic systems with ring connections. Employing the direct design method to design the synchronization and anti-synchronization controllers which can transform the error systems into a stable system with special anti-symmetric structure. Some simple stability criteria are then derived for reaching the synchronization in such systems. It is proved that these criteria not only are easily verified, but also improve and generalize previously known results. Finally, numerical examples are provided to demonstrate the effectiveness of the theoretical. [ABSTRACT FROM AUTHOR]

• As a first attempt, the SMC problem of PDT switched nonlinear systems is addressed, and the dynamic ET mechanism is introduced to alleviate the limited bandwidth resources, which is more general. • With the dynamic ET mechanism, a new switched SMC law is constructed with the PDT switching strategy that is more general than DT and ADT switching strategies, which can match the subsystem one by one more reasonably and reduce the conservativeness to some extent. • Based on the Lyapunov function approach, some sufficient conditions, which can ensure both the reachability and globally uniform exponential stability with an H ∞ performance of the closed-loop PDT switched system, are derived. In this paper, the H ∞ sliding mode control problem of persistent dwell-time switched nonlinear systems is investigated. Due to the limited bandwidth resources, a dynamic event-triggered mechanism is introduced to alleviate the transmission burden. Considering that the system parameters are switched in accordance with the persistent dwell-time switching strategy, a novel switched sliding mode control law is constructed. Then, drawing on the Lyapunov function approach, sufficient conditions are derived, which not only ensure the reachability of the sliding region around the specified sliding surface but also guarantee the globally uniform exponential stability of the system with an H ∞ performance. Moreover, the specific form of the controller gains is derived by utilizing an efficient decoupling method. Eventually, the validity of the proposed method is validated by two numerical examples. [ABSTRACT FROM AUTHOR]

This paper studies the sliding mode control method for coupled delayed fractional reaction-diffusion Cohen–Grossberg neural networks on a directed non-strongly connected topology. A novel fractional integral sliding mode surface and the corresponding control law are designed to realize global Mittag-Leffler synchronization. The sufficient conditions for synchronization and reachability of the sliding mode surface are derived via the hierarchical method and the Lyapunov method. Finally, simulations are provided to verify our theoretical findings. [ABSTRACT FROM AUTHOR]

• A new concept, called fixed-time funnel boundary (FFB), is defined for the first time in this paper. • This work is first to introduce a new control mechanism for TCP/AWM system to ensures that the tracking error satisfies certain transient performances. • The proposed control scheme has the better performance and faster convergent rate than results without FFB or ACT. In this paper, TCP/AWM network congestion control problem is investigated. First, a new funnel boundary (FB) is proposed. And then, in the framework of backstepping, a new control technique is given by virtue of free-will arbitrary time stability and the novel FB, which guarantees that the tracking error converges to the origin within an arbitrary setting time. Besides, the transient performances of the error can also be achieved. Finally, simulation results verify the efficacy and superiority of the presented theoretical findings. [ABSTRACT FROM AUTHOR]

This paper focuses on the distributed adaptive finite-time control problem for nonlinear multi-agent systems (MASs). The fast practical finite-time criterion is extended to MASs. As such, a distributed adaptive finite-time protocol is established based on the recursion algorithm and neural networks (NNs), which provides a solution to cope with the adaptive finite-time regulate problem. Moreover, the proposed scheme is able to address the singular problem existing in the adaptive fuzzy/NN practical finite-time control. Furthermore, a simpler adaptive law is constructed. Finally, the presented method is applied to multiple robotic systems to illustrate its effectiveness. [ABSTRACT FROM AUTHOR]

This paper is devoted to investigating the impact of the recurrence of rumors and individual behaviors and control strategies related to rumor spreading in online social networks. To do this, a novel susceptible-hesitating-infected-latent-recovered (SHILR) rumor propagation model in heterogeneous networks is presented. Firstly, based on the relevant mean-field equations of the model, the threshold value is examined to demonstrate the existence and stability of rumor-free/spreading equilibrium with the help of the algebraic method. Secondly, the global stabilities of the equilibria are analyzed through applying Lyapunov stability theory and LaSalle's invariance principle. Next, the optimal control is proposed by taking advantage of Pontryagin's maximum principle for reducing the number of infected individuals with minimum cost. Moreover, some numerical examples are carried out to test the theoretical results. Finally, combined with practice, a model application is presented. [ABSTRACT FROM AUTHOR]

In this paper, the leader‐following consensus problem of general linear multi‐agent systems with distributed dynamic event‐triggered control is studied. A distributed controller with estimated states is designed and a dynamic event‐triggered communication protocol with an auxiliary variable is proposed for each agent to update its triggering threshold. Under this control strategy, the systems only need the states in triggering instants as a result of the utilisation of estimated states. The existence of a dynamic threshold guarantees fewer triggering times, in contrast to the conventional static one. Meanwhile, the estimator is designed between the triggering instants to reduce the state shift, the next state of each agent depends on the local information of itself and its neighbours rather than global information. Then, the multi‐agent systems can achieve consensus without Zeno behaviour. Finally, a simulation example is offered to verify the effectiveness and feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, the leader‐following consensus problem of general linear multi‐agent systems with distributed dynamic event‐triggered control is studied. A distributed controller with estimated states is designed and a dynamic event‐triggered communication protocol with an auxiliary variable is proposed for each agent to update its triggering threshold. Under this control strategy, the systems only need the states in triggering instants as a result of the utilisation of estimated states. The existence of a dynamic threshold guarantees fewer triggering times, in contrast to the conventional static one. Meanwhile, the estimator is designed between the triggering instants to reduce the state shift, the next state of each agent depends on the local information of itself and its neighbours rather than global information. Then, the multi‐agent systems can achieve consensus without Zeno behaviour. Finally, a simulation example is offered to verify the effectiveness and feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

Hopf bifurcation of a delay differential equation modeling two-enterprise interaction mechanism is investigated. For the demands of computer simulation applications, the discrete-time two-enterprise interaction model is proposed by using the forward Euler scheme. By discussing the characteristic equation of linearized part, the local stability criterion of the positive fixed point is presented. Time lag is selected as the bifurcation parameter. Then it is demonstrated that variation of time lag may result in Hopf bifurcation. Furthermore, derivations for the directions and stability of Hopf bifurcation are also given. For the purpose of controlling Hopf bifurcation, a time-delayed feedback control scheme is proposed. Finally, we simulate the main results. [ABSTRACT FROM AUTHOR]

In this paper, the synchronization problem of inertial neural networks with time-varying delays and generally Markovian jumping is investigated. The second order differential equations are transformed into the first-order differential equations by utilizing the variable transformation method. The Markovian process in the systems is uncertain or partially known due to the delay of data transmission channel or the loss of data information, which is more general and practicable to consider generally Markovian jumping inertial neural networks. The synchronization criteria can be obtained by using the delay-dependent Lyapunov–Krasovskii functionals and higher order polynomial based relaxed inequality (HOPRII). In addition, the desired controllers are obtained by solving a set of linear matrix inequalities. Finally, the numerical examples are provided to demonstrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper discusses the complex dynamic behavior of a novel chaotic system, which was firstly established by introducing a memristor into a similar Chen's system. Then by choosing a as the key parameter, we analyze the stability of memristor system based on eigenvalue theory. It is also found that when a cross some critical values, the system can exhibit Neimark–Sacker bifurcation and chaos behaviors. Some numerical simulations including phase diagrams and maximum Lyapunov exponent graph of the memristor-based systems are presented to verify the existence of chaos attractors. Finally, to make the results of this paper useful in the actual situation, such as the design of chaos security algorithm, analog electronic circuit of memristor chaotic system is designed. [ABSTRACT FROM AUTHOR]

This paper investigates the synchronization control issue for a set of switched inertial neural networks in the discrete-time domain, in which the persistent dwell-time switching law is employed to depict the switchings among system parameters. Thereinto, the foregoing networks having the second-order differential equations are degraded to the first-order differential ones through applying the variable transformation. In addition, in order to cope with random gain fluctuations caused by noise or harsh environments, in the controller, two random variables obeying the Bernoulli distribution are employed to simulate the occurrence of gain fluctuations. Based on Lyapunov stability theory, persistent dwell-time concept and stochastic analysis theory, some sufficient criteria are derived under which the synchronization error system is exponentially mean-square stable with a prescribed l 2 - l ∞ property. Finally, a numerical example, including some illustrative simulations, is given to present the feasibility of the derived analytical results. [ABSTRACT FROM AUTHOR]

• This work proposes MPFFPSDC, a novel deep learning algorithm based on multi-pooling feature fusion for DDS prediction. • In MPFFPSDC, we introduce a new pooling method, which uses attention mechanism to aggregate atomic feature vectors into a drug feature vector. • MPFFPSDC can keep maintain the symmetry of drug inputs to eliminate the inconsistency of predictive results caused by different drug input sequences. • Compared with existing state-of-the-art methods, MPFFPSDC achieves higher predictive accuracy. Drug combination therapies are common practice in the treatment of cancer, but not all combinations result in synergy. As traditional screening approaches are restricted in their ability to uncover synergistic drug combinations, computer-aided medicine is becoming a increasingly prevalent in this field. In this work, a predictive model of potential interactions between drugs named MPFFPSDC is presented, which can maintain the symmetry of drug inputs and eliminate inconsistencies in predictive results caused by different drug inputting sequences or positions. The experimental results show that MPFFPSDC outperforms comparative models in major performance indicators and exhibits better generalization for independent data. Furthermore, the case study demonstrates that our model can capture molecular substructures that contribute to the synergistic effect of two drugs. These results indicate that MPFFPSDC not only offers strong predictive performance, but also has good model interpretability that may provide new insights for the study of drug interaction mechanisms and the development of new drugs. [ABSTRACT FROM AUTHOR]

Inspired by the prescribed performance control (PPC), a new performance function, called finite-time performance function (FTPF), is first defined in this paper, by which a novel finite-time control design process is introduced. This work is also the first to solve the finite-time control issue for a class of transmission control protocol/active queue management (TCP/AQM) networks. Meanwhile, with the aid of FTPF, PPC and neural networks, a new adaptive practically pre-assigned finite-time controller is designed, which guarantees that the queue length q (t) tracks the desired queue q ref in finite-time and all the signals of the closed-loop system are semi-globally practical finite-time stable (SGPFS). Finally, simulation results are shown to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: This paper focuses on the problem of simultaneous fault detection and antisaturated control for an inverted pendulum control system. The system input is the acting control voltage that generates the force on the cart. However, the acting control voltage is obviously limited by the voltage amplitude. This means that the actuator of the constructed system model is saturated. Hence, the detector/controller unit produces two signals: the detection and antisaturated control. In this process, the effects of disturbances and noises on detection and control purposes need to be suppressed. Finally, two simulation examples verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we investigate the pinning synchronization of directed complex network with time‐varying delay under the adaptive aperiodically intermittent control. First, the model of a directed complex network is established, the interaction graph of which is not required to contain a directed spanning tree, and for which an adaptive aperiodically intermittent feedback controller is well designed. In addition, based on the Lyapunov function method and linear matrix inequality technique, several asymptotical synchronization criteria are derived via an aperiodically intermittent strategy, which is superior to the periodical scheme. Moreover, the adaptive strategy providing the exponential convergence rate and the pinning algorithm demonstrating how to select pinned nodes are presented. Finally, numerical examples are provided to illustrate the effectiveness of the derived theoretical results. [ABSTRACT FROM AUTHOR]

The target of this article is to study almost periodic dynamical behaviors for complex-valued recurrent neural networks with discontinuous activation functions and time-varying delays. We construct an equivalent discontinuous right-hand equation by decomposing real and imaginary parts of complex-valued neural networks. Based on differential inclusions theory, diagonal dominant principle and nonsmooth analysis theory of generalized Lyapunov function method, we achieve the existence, uniqueness and global stability of almost periodic solution for the equivalent delayed differential network. In particular, we derive a series of results on the equivalent neural networks with discontinuous activation functions, constant coefficients as well as periodic coefficients, respectively. Finally, we give a numerical example to demonstrate the effectiveness and feasibility of the derived theoretical results. [ABSTRACT FROM AUTHOR]

• The stochastic complex networks model with internal and coupled delays is established. • The pinning impulsive controller without sign function is designed to achieve finite-time synchronization. • A unified finite-time synchronization criterion is derived under synchronizing impulses and desynchronizing impulses. • The pinned nodes are related to the error values of the nodes, which improves the utilization of control resources. This paper mainly considers the finite-time synchronization (FTS) of stochastic complex networks with mixed delays via pinning impulsive control. Firstly, a novel pinning impulsive controller is introduced by selecting the nodes of the stochastic complex network with large error norms, which not only saves control resources, but also ensures that stochastic complex network with mixed delays can realize FTS. It is worth noting that the finite-time control strategy constructed is not affected by the sign function. Secondly, a uniform FTS criterion under synchronizing impulses and desynchronizing impulses is derived based on the idea of average impulsive interval. Moreover, the obtained sufficient condition are further extended to general stochastic complex networks and stochastic complex networks with mixed delays under delayed impulses. Finally, the feasibility of the theory results is verified by a numerical example. [ABSTRACT FROM AUTHOR]

This paper studies the discrete-time slow state feedback fault-tolerant controller design issue for Takagi-Sugeno fuzzy-model-based Markov jump singularly perturbed nonlinear systems, in which transition probabilities of systems subject to persistent dwell-time switching mechanism. In order to eliminate the influence of actuator faults and provide a more flexible control strategy, a multi-objective fault-tolerant controller is designed to ensure the stability of the system and meet the requirements of various performance indexes. The goal is to establish some sufficient conditions, which ensure the mean-square exponential stability and the extended dissipativity performance of the closed-loop system. Furthermore, the gains of the designed controller can be obtained by solving the problem of convex optimization. Finally, the availability of the designed controller is demonstrated by a numerical example and a tunnel diode circuit system. [ABSTRACT FROM AUTHOR]

• This is the first work to design a finite-time controller with a new adaptive mechanism by constructing a novel preset region. The dynamical change of the control gain is dependent on the case whether the state trajectories enter or leave a pre-designed domain or not. • The novel adaptive control strategy is presented to avoid the overestimation of the developed adaptive gain based on the adding a power integrator technique and the Lyapunov method. Meanwhile, the novel practical adaptive finite-time controller could improve the dynamic performance of systems and reduce the potential chattering influence. • In practical applications, the system states are usually finite-time stabilized to a neighborhood of the origin, rather than the origin. From this point of view, the investigation of practical finite-time stability (PFTS) for nonlinear systems is more meaningful. In this paper, the PFTS of second-order nonlinear systems has been verified under the developed adaptive finite-time controller. This paper considers the practical adaptive finite-time stabilization problem for a class of second-order systems. Based on the adding a power integrator technique and the adaptive method, a novel adaptive control strategy is presented to assure the trajectories of the closed-loop systems converge to a small neighborhood of the origin in a finite time. Under the adaptive control strategy, the dynamical change of control gain is dependent on the cases whether the state trajectories enter or leave a pre-designed domain or not. In addition, the practical finite-time stability (PFTS) of systems is testified via utilizing the Lyapunov method and adaptive mechanism. Last of all, the validity of the obtained practical adaptive finite-time algorithm is verified via simulation results. [ABSTRACT FROM AUTHOR]