Search

Communication networks grow exponentially in this globalization era; thus, the network traffic modelling and prediction plays a crucial role in network management and security warning. Solely, the multi-step network traffic prediction may involve greater errors hence worsening prediction performance. To overcome this problem, an optimized echo state network model with selective error compensation is proposed. In the optimized echo state network-based multi-step prediction model, an improved fruit–fly optimization algorithm based on cloud model (named LVCMFOA) is used to select optimum values of four key parameters of the model. The proposed LVCMFOA algorithm uses the levy-flight function to redefine the generation of the fruit–fly population, which can randomly change the search radius and help getting out of a possible local optimal solution and prevent local optimum. To reduce the calculation time but improve the prediction accuracy simultaneously, a sophisticated selective error compensation strategy employing the variable sliding window technology is proposed so as to avoid the error accumulation problem in the multi-step prediction. The effectiveness of the proposed method is verified by applying it to Henon mapping chaotic series, Mackey–Glass chaotic series and two public network traffic data sets all known in the literature. [ABSTRACT FROM AUTHOR]

With the complexity of the network system rapidly increasing, network traffic prediction has great significance for the safety pre-warning of the network load, network management and control, and improvement of the quality of the network service. In this paper, the time series analysis is used for the network traffic prediction, and a prediction method combined with an optimized unscented Kalman filter (UKF) by an improved fruit fly algorithm (IFOA) and echo state network (ESN) is proposed, which is named by IFOA-UKF-ESN. The researches mainly solve the problem that the prediction accuracy might be greatly affected by the actual network traffic data with unknown and time-varying noises. UKF is used to train the best state vector (formed by spectral radius, scale of the reservoir, scale of the input units and connectivity rate) of ESN; and the proposed IFOA algorithm is proposed to optimize the weights of the predicted state value and the covariance in UKF, which makes UKF have adaptive ability for unknown and time-varying noise. Three actual network traffic data sets with different Gaussian white noise distributions are constructed for experiments, and the experimental results show that the proposed prediction method makes an average improvement by reducing at least 20.60%, 43.23% and 41.85% of RMSE, at least 23.66%, 52.38% and 47.50% of MAE, and at least 23.58%, 52.10% and 47.28% of MAPE, which verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A new anti-disturbance control scheme is proposed for a class of nonlinear systems subjected to multi-source disturbances. The uncertain multi-source disturbances are classified into two parts; one is the harmonic and constant disturbance with partial known information, which can be formulated by an exogenous system, and the other consists of unknown time-varying disturbances. The nonlinear disturbance observer (NDO) is constructed to estimate the first kind of disturbance. By integrating the NDO with an adaptive backstepping technique, a novel composite anti-disturbance control scheme is proposed for the nonlinear systems. Then the stability analysis of the closed-loop system is presented. The simulation of a numerical example is given to demonstrate the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

A new elegant anti-disturbance control and estimation strategy is presented for a class of nonlinear systems subject to multiple disturbances. As a class of strong-coupled nonlinear systems, the models have measurement error, friction, varying load, and unmodelled dynamics, which can be characterized as two types of disturbances in this framework. One is supposed to be generated by an exogenous system with uncertainty, which represents the disturbance with partial known information. The other is a bounded external disturbance in the H2-norm. The nonlinear robust disturbance observer (DO) based on regional pole placement theory is constructed separately from the controller design to estimate and reject the first type of disturbance, and terminal sliding-mode (TSM) control is used to attenuate the second type of disturbance. By integrating the nonlinear robust DO with TSM control, a new elegant anti-disturbance control scheme based on a robust DO is proposed for a kind of nonlinear system, which can further improve the ability of anti-disturbance for nonlinear systems. Simulations are given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]