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The decorrelated neural network ensemble (DNNE) algorithm can be used to construct an effective soft measuring model through an analytical solution comprising submodels of multiple randomized neural networks. However, DNNE exhibits one major shortcoming: the scope of the random input weights and biases is set to a default range of [−1, 1], which cannot ensure the universal approximation capability of the resulting prediction model. The three other learning parameters including the number of the ensemble submodels’ hidden nodes, ensemble size, and regularizing factor of DNNE are also data dependent. Moreover, DNNE suffers from high computation complexity when processing a high-dimensional large-scale dataset. Feature selection can improve the interpretation of the model. To address the above-mentioned problems, a combinatorial optimization method based on the adaptive genetic algorithm is used to simultaneously optimize the input features and learning parameters of the DNNE model. The evolution processes of these modeling parameters are demonstrated in detail. Simulation results based on four datasets with different dimensions and sizes validate the effectiveness of the proposed approach. The results also indicate that the random parameter scope assignment significantly influences the generalization performance and the other learning parameters of the prediction model. [ABSTRACT FROM AUTHOR]

This paper deals with a class of neutral shunting inhibitory cellular neural networks with time-varying delays and D operator. Using the differential inequality theory and Lyapunov functional method, a set of sufficient conditions which ascertains that the existence and exponential stability of anti-periodic solutions of neutral shunting inhibitory cellular neural networks with time-varying delays and D operator are derived. Computer simulations are delineated to substantiate the correctness of our theoretical predictions. The obtained results of this paper are new and complement some earlier works. [ABSTRACT FROM AUTHOR]

In this paper, we propose a general nonlinear load-capacity model to investigate the controllability robustness of scale-free networks under the cascading failure triggered by removing the highest-load edge. Based on the nonlinear model, the effect of the capacity distribution is studied to enlighten how to reduce network cost by adjusting capacity distribution for controllability robustness against cascading failures. By performing numerical simulations on Barabási–Albert (BA) scale-free networks and some real networks, we find that the unoccupied capacities of nearly highest-load edges make little impact on controllability robustness, and the high-load edges become more important to controllability robustness as the power law exponent increases. In particular, for scale-free networks with small-degree power law exponents or small out-in degree correlation coefficients, by properly reducing the unoccupied capacities of high-load edges, we can effectively cut down the network cost without reducing the controllability robustness. [ABSTRACT FROM AUTHOR]

This article deals with the design and real-time implementation of three second order sliding mode controllers for the altitude tracking of a quadrotor aircraft. A comparative study based on the analysis of the tracking error was performed in order to determine the controller presenting the best performance in a real-time application at outdoors environments. The compared strategies were the Classical First Order Sliding Mode Controller, the Super Twisting Sliding Mode Controller, the Modified Super Twisting Sliding Mode Controller and the Nonsingular Terminal Super Twisting Sliding Mode Controller. The last three controllers are based on the second order sliding mode technique, and they ensure robustness with respect to modeling errors even under external disturbances while reducing the chattering phenomenon in comparison with first order sliding mode controllers. Lyapunov stability theory is used to prove convergence in finite time of the altitude tracking error in the different proposed control laws. In order to demonstrate the effectiveness of the proposed solutions, an extensive set of simulation and real-time experimental results are presented. [ABSTRACT FROM AUTHOR]

This paper introduces a novel metaheuristic methodology to address the Multiple Measurement Vectors problem using a well-known greedy search strategy. A modified version of Tabu Search algorithm is utilized to determine a precise estimation of row support and then joint sparse samples are reconstructed using MMSE criterion. The proposed approach is more robust to the sparsity order variations and noise uncertainty, in comparison with the conventional MMV problem solvers. Furthermore, to avoid wastage of the sampling resources and reduce the implementation costs, a two-step joint sparse recovery framework is developed which the first step predicts and adjusts the optimum sampling rate and the second one reconstructs signal vectors applying obtained sampling rate. Numerical simulations demonstrate the superiority of proposed technique for both improving the performance and reducing the computational complexity and sampling costs. [ABSTRACT FROM AUTHOR]

This paper proposes a new local texture descriptor, called quad binary pattern (QBP). Compared with local binary pattern (LBP), the QBP is with stronger robustness for feature extraction under complex scene (e.g., luminance change, similar target and background color) and with lower computational complexity. To demonstrate its effectiveness, the proposed QBP is further applied on the mean-shift tracking, in which a joint color-QBP model is developed to effectively represent the color and texture characteristics of the target region. Extensive simulation results have demonstrated that the proposed algorithm is able to improve the tracking speed and accuracy, compared with the standard mean-shift tracking and joint color-LBP model based mean-shift tracking. [ABSTRACT FROM AUTHOR]

In line of the research on leader-following consensus, this paper studies the consensus problem for the first-order multi-agent system with a smart leader. Different from previous works where the leader is independent of all the other agents, a kind of leader, called smart leader, is proposed, which can gain and utilize its neighboring followers' position information. Whether such information will be used in adjusting the control algorithm for the smart leader is decided by an event-trigger function. The theory and simulation results show that the proposed model can gain advantage in robustness and fault-tolerance ability with the traditional one. Moreover, a sufficient condition is provided, which can ensure that the position-tracking error and velocity-tracking error are bounded when some actuator faults occur on some of the followers in the framework of the fixed topology. In addition, similar results are also given under switching topology. Finally, some simulation examples are presented for illustration. [ABSTRACT FROM AUTHOR]

This paper presents a robust H ∞ output-feedback vehicle lateral motion control strategy considering network-induced delay and tire force saturation. The unavoidable time delay in the in-vehicle networks degrades the control performance, and even deteriorates the system stability. In addition, the tire lateral force suffers saturation phenomenon, which also deteriorates the control effect in extreme driving conditions. To handle the network-induced control delay and tire force saturation, a robust H ∞ controller is presented to regulate the vehicle lateral motion. An output-feedback control schema, which does not need the vehicle lateral velocity, is designed to achieve the desired control performance and reduce the cost of control system. The tire cornering stiffness uncertainty and external disturbances are also considered in the controller design to improve the robustness of the proposed controller. The comparative simulation results based on Carsim-Simulink joint simulation verify the effectiveness and robustness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

In this paper, by taking into account stochastic disturbances induced by the integration of large number of renewable energy resources into power grid, the robust H ∞ load frequency control problem for multi-area power system with time delay is discussed in detail. Considering time delay induced by signal transmission between different control areas, two different controller designing strategies, namely the state feedback controller and the delayed state feedback controller designing strategies are proposed, respectively. Based on Lyapunov stability theory, several less conservative stability conditions are developed in the form of linear matrix inequalities to ensure the frequency stability of multi-area power system, where the optimal attenuation level can also be obtained by solving the constrained optimization problem. Finally, simulation results based on a two-area interconnected power system are provided to demonstrate the usefulness and effectiveness of the developed results. [ABSTRACT FROM AUTHOR]

This paper investigates distributed containment control problems for second-order nonlinear multi-agent systems with dynamic leaders. In the presence of bounded unknown nonlinearity, a distributed control algorithm based on High-frequency Feedback Robust Control is proposed. Under this algorithm, a sufficient condition is proposed to ensure that all the followers will converge to the dynamic convex hull formed by dynamic leaders and the containment error can be ultimately bounded for the directed graph where at least one leader has a directed path to each follower and the communication among the followers are undirected. Numerical simulations are also presented to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper addresses the robust consensus problem of fractional-order linear multi-agent systems with positive real uncertainty where the fractional-order α satisfies 0 < α < 2 . For multi-agent systems with fixed undirected topology, a distributed protocol is proposed based on the information of second-order neighbors. Then, new LMI-based sufficient conditions for robust consensus of fractional-order multi-agent systems are obtained and the feedback matrix is also determined accordingly. Finally, numerical simulation examples are presented to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper considers the cooperative robust output regulation for second-order discrete-time multi-agent systems. Each agent could access the information with its neighbors under the directed network topology. A distributed dynamic state feedback control law is designed. A stability region is given for the discrete-time multi-agent systems, and internal model method is used for the robust output regulation problem. At last, numerical simulation is utilized to show the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

A new fault detection scheme based on the proposed robust one class support vector machine (1-class SVM) is constructed in this paper. 1-class SVM is a special variant of the general support vector machine (SVM) and since only the normal data is required for training, 1-class SVM is widely used in anomaly detection. However, experiments show that 1-class SVM is sensitive to the outliers included in the training data set. To cope with this problem, a robust 1-class SVM is proposed in this paper. With the designed penalty factors, the robust 1-class SVM can depress the influences of outliers. Fault detection scheme is constructed based on the robust 1-class SVM. The simulation example shows that the robust 1-class SVM is superior to the general 1-class SVM, especially when the training data set is corrupted by outliers, and the fault detection scheme based on robust 1-class SVM presents satisfactory performances. [ABSTRACT FROM AUTHOR]

In this paper, the robust tracking control scheme is proposed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with input saturation and unknown external disturbance based on the recurrent wavelet neural network disturbance observer (RWNNDO) and the backstepping technique. And then, the developed robust tracking control scheme is applied to an unmanned aerial vehicle (UAV) system. To handle the input saturation, a hyperbolic tangent function and a Nussbaum function are employed, and the dynamic surface method is applied to solve the problem of "explosion of complexity" in backstepping control. It is proved that the proposed control scheme can guarantee that all signals of the closed-loop system are bounded through the Lyapunov analysis. Simulation results are presented to demonstrate the effectiveness of the proposed control scheme for uncertain MIMO nonlinear systems. [ABSTRACT FROM AUTHOR]