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In the paper, an improved nonsingular fast terminal sliding mode (INFTSM) guidance law with impact angle constraints is proposed. The guidance law, which employs a double power reaching law and an attractor with negative exponential factor, has a fast speed no matter far from the sliding surface or approach. For the maneuvering targets, extended state observer is designed through which the unknown...
We consider the problem of modeling data matrices with locally low rank (LLR) structure, a generalization of the popular low rank structure widely used in a variety of real world application domains ranging from medical imaging to recommendation systems. While LLR modeling has been found to be promising in real world application domains, limited progress has been made on the design of scalable algorithms...
This paper proposes a new continuous terminal sliding mode guidance law for three-dimensional missile guidance system under maneuvering target with consideration of second-order autopilot dynamics. The proposed guidance law not only guarantees the azimuth rate and elevation rate of guidance system converge to zero in finite time but also ensures the continuity of control action. Simulations of a practical...
This paper presents a new control methods based on a novel reaching law to overcome slow convergence rate and severe chattering in sliding mode control of permanent magnet synchronous motors (PMSM). The constant reaching law converges slowly during large systematic errors. Although the power reaching law converges fast during large error, it chatters severely while approaching to the sliding surface...
In this paper, a particle swarm optimization method with a new strategy for inertia weight has been considered. The author abandoned the commonly used linear inertia weight and proposed a new dynamic inertia weight based on fitness of the particles. The new weight is a function of the best and the worst fitness of the particles. The considered NIWPSO algorithm was tested on a set of benchmark functions...
This paper investigates horizontal crossover (HC) and stability-based adaptive inertia weight (SAIW) strategies for comprehensive learning particle swarm optimization. HC applies arithmetic crossover on all the dimensions of two different personal best positions. SAIW adaptively adjusts the inertia weight and acceleration coefficient for each particle on each dimension. Experimental results on various...
Particle Swarm Optimization (PSO) is fast and popular algorithm to find the optimum value of non-linear and multi-dimensional function. However, it often easily trapped into local optima because the particles move closer to the best particle quickly. This paper purposes a new algorithm called Multi-Group Particle Swarm Optimization with Random Redistribution (MGRR-PSO) that tried to solve the weakness...
We present a simple and computationally efficient algorithm, based on the accelerated Newton's method, to solve the root finding problem associated with the projection onto the ℓ1-ball problem. Considering an interpretation of the Michelot's algorithm as Newton method, our algorithm can be understood as an accelerated version of the Michelot's algorithm, that needs significantly less major iterations...
The simultaneous perturbation stochastic approximation(SPSA) belongs to the class of iterative gradient-free algorithm. However, because of its slow convergence rate, the experimental effect is not ideal for large-scale problems. In order to accelerate the SPSA algorithm, this paper proposes a parallelized combined direction SPSA algorithm. Gradient directions among the master and slaves are combined...
One of the most classic algorithms for association rules mining is the Apriori algorithm. But it can't satisfy the requirement as the increasing scale of the data. It has some disadvantages such as scanning database too many times, setting support and confidence thresholds artificially. Particle swarm optimization is one of the classic heuristic algorithms and some researchers has used it to association...
In the paper a modification enabling acceleration of the rate of convergence for LMS-like on-line identification and adaptation algorithms is proposed. This is based on an artificial decaying of initial conditions in recursive identification as well as adaptation algorithms. The decaying is done using a set of the most recent measurements. Properties of the algorithms with the proposed modification...
In this paper, a matrix polynomial whose coefficients are matrices is first defined. Its predictive model, called as the Matrix Polynomial Predictive Model (MPPM), is then derived. When the loading matrices of a decomposed tensor in the Alternating Least Squares (ALS) are replaced by the predicted ones of the MPPM, a new ALS algorithm with the MPPM (ALS-MPPM) is proposed. Analyses show that the convergent...
We extend our previous work on learning smooth graph signals from a small number of noisy signal samples. Minimizing the signal's total variation amounts to a non-smooth convex optimization problem. We propose to solve this problem using a combination of Nesterov's smoothing technique and accelerated coordinate descent. The resulting algorithm converges substantially faster, specifically for graphs...
In this study, the problems are how to determine the locations and the size of capacitors in the buses of radial distribution system. The proposed method uses loss sensitivity factors (LSF) to identify the buses requiring compensation and then an accelerated particle swarm optimization (APSO) algorithm is used to determine the sizes of the capacitors to be installed. The proposed method is tested...
Path planning based on heuristic optimization method is developed to simplify the path planning issues into optimization problems. Particle Swarm Optimization (PSO) is one of the heuristic optimization methods often used because of its simplicity, easy to implement and has few parameters to set. However, the basic PSO algorithm has difficulties balancing exploration and exploitation, and suffer from...
Low-rank sparse tensor factorization is a populartool for analyzing multi-way data and is used in domainssuch as recommender systems, precision healthcare, and cybersecurity.Imposing constraints on a factorization, such asnon-negativity or sparsity, is a natural way of encoding priorknowledge of the multi-way data. While constrained factorizationsare useful for practitioners, they can greatly increasefactorization...
Radial Basis Function(RBF) mesh deformation method has been widely used in CFD simulations with moving boundaries due to its high robustness and accuracy. The original implementation of the RBF mesh deformation method in OpenFOAM(a widely used CFD software) is purely serial with relatively low computational performance. To reduce the time cost of the mesh motion in large-scale simulations, this paper...
A special class of recurrent neural network, termed Zhang neural network (ZNN), has been recently proposed for solving various dynamic problems, and has shown excellent performance in the real-valued domain. In this paper, a new complex-valued ZNN model (termed CVZNN model) is firstly proposed and investigated for online solution of dynamic complex-valued matrix pseudoinverse. Particularly, a novel...
A finite time convergent sliding-mode guidance law with hysteresis switching is proposed for the guidance considering thrust constraint and second-order dynamics of divert thruster. Firstly, the mathematical model of relative movement considering the second-order dynamic delay of engine is set up according to the planar relative motion of missile and target. Secondly, based on the principle of nullifying...
With the explosive growth of user load data in power consumption information collection and load control systems, traditional computing frameworks and methods are faced with tremendous computational pressure when dealing with massive user load clustering and carrying out load characteristic analysis. In this paper, with a view to increasing accuracy and computational power of graphic process unit...
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