In this paper, a dynamic evaluation of the multistate weighted k-out-of-n:F system is presented in an unreliability viewpoint. The expected failure cost of components is used as an unreliability index. Using failure c...
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In this paper, a dynamic evaluation of the multistate weighted k-out-of-n:F system is presented in an unreliability viewpoint. The expected failure cost of components is used as an unreliability index. Using failure cost provides an opportunity to employ financial concepts in system unreliability estimation. Hence, system unreliability and system cost can be compared easily in order to making decision. The components' probabilities are computed over time to model the dynamic behavior of the system. The whole system has been assessed by recursive algorithm approach. As a result, a bi-objective optimization model can be developed to find optimal decisions on maintenance strategies. Finally, the application of the proposed model is investigated via a transportation system case. Matlab programming is developed for the case, and genetic algorithm is used to solve the optimization model. Copyright (c) 2016 John Wiley & Sons, Ltd.
A set of maximal non-intersecting diagonals can decompose a polygon into triangles, and the edges and diagonals can be converted into the external and internal nodes of a strictly binary tree. This paper gives algorit...
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A set of maximal non-intersecting diagonals can decompose a polygon into triangles, and the edges and diagonals can be converted into the external and internal nodes of a strictly binary tree. This paper gives algorithms to generate all types of triangulations and triangulations at random. Based on that, this paper gives an algorithm to generate strictly binary trees at random. The experimental results show that the numbers of various shapes of strictly binary trees generated are nearly equal. The algorithm to generate strictly binary trees at random can be transformed to the algorithm to randomly generate binary trees.
This paper discusses the adaptive modelling algorithm of the non-linear Errors-in-Variables (EIV) system. The non-linear EIV model is designed as a Wiener model with noisy input and noisy output measurements. The mode...
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This paper discusses the adaptive modelling algorithm of the non-linear Errors-in-Variables (EIV) system. The non-linear EIV model is designed as a Wiener model with noisy input and noisy output measurements. The model consists of a moving average model linear subsystem and a piece-wise linear function as the non-linear subsystem. In order to ensure the online real-time updating of system parameters, the recursive form is designed for the algorithm to keep its adaptive properties and improve the parameters' identification efficiency. The derivation process of the algorithm ensures the recursive calculation results converge to their corresponding true values. The validity and convergence of the algorithm is confirmed by a simulation experiment with the help of Matlab.
The stochastic Newton recursive algorithm is studied and utilized for identifying the dual-rate model which is derived through the polynomial transformation technique. The main advantage of the algorithm is that it ha...
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The stochastic Newton recursive algorithm is studied and utilized for identifying the dual-rate model which is derived through the polynomial transformation technique. The main advantage of the algorithm is that it has the extensive form and may earn more possible convergence properties with flexible parameters. The primary problem is that the sample covariance matrix may be singular when encountering numbers of model parameters and (or) no general input signal, which hinders the identification process. Thus, the main contribution is adding one symmetric positive definite matrix to the recursion of the sample covariance matrix. This simple approach solves the problem effectively. Two improved stochastic Newton recursive algorithms are then proposed for time-invariant and time-varying systems. The consistent and bounded convergence conclusions of the corresponding algorithms are drawn respectively. The final illustrative examples demonstrate the effectiveness and the convergence properties of the recursive algorithms.
A simple and straightforward fast iterative method is presented for computing the inverse and determinant of any square matrix by successively applying order condensation and order expansion in an iterative process. A...
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A simple and straightforward fast iterative method is presented for computing the inverse and determinant of any square matrix by successively applying order condensation and order expansion in an iterative process. Applying the optimal iteration process, which comprises only some 20 lines of the MATLAB source code (using only simple elementary arithmetical operations), the inverse matrix can be computed within minutes from any given square matrix, even of relatively large size (such as 999), with real or complex entries, and irrespective of whether the matrix is singular or nonsingular.
In this paper we derive the stochastic differentials of the conditional central moments of the nonlinear filtering problems, especially those of the polynomial filtering problem, and develop a novel suboptimal method ...
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In this paper we derive the stochastic differentials of the conditional central moments of the nonlinear filtering problems, especially those of the polynomial filtering problem, and develop a novel suboptimal method by solving this evolution equation. The basic idea is to augment the state of the original nonlinear system by including the original states' conditional central moments such that the augmented states form a so-called bilinear system after truncating. During our derivation, it is clear to see that the stochastic differentials of the conditional central moments of the linear filtering problem (i.e., f, g and h are all at most degree one polynomials) form a closed system automatically without truncation. This gives one reason for the existence of optimal filtering for linear problems. On the contrary, the conditional central moments form an infinite dimensional system, in general. To reduce it to a closed-form, we let all the high enough central moments to be zero, as one did in the Carleman approach (Germani et al., 2007). Consequently, a novel suboptimal method is developed by dealing with the bilinear system. Numerical simulation is performed for the cubic sensor problem to illustrate the accuracy and numerical stability. (C) 2015 Elsevier Ltd. All rights reserved.
This work focuses on numerical algorithms for approximating the ergodic means for suitable functions of solutions to stochastic differential equations with Markov regime switching. Our main effort is devoted to obtain...
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This work focuses on numerical algorithms for approximating the ergodic means for suitable functions of solutions to stochastic differential equations with Markov regime switching. Our main effort is devoted to obtaining the convergence and rates of convergence of the approximation algorithms. The study is carried out by obtaining laws of large numbers and laws of iterated logarithms for numerical approximation to long-run averages of suitable functions of solutions to switching diffusions. (C) 2015 Elsevier B.V. All rights reserved.
In this paper, a new approach to the harmonic estimation based on the linear transformation named Taylor-Fourier transform has been proposed. A multiple-resonator-based observer structure has been used. The output tap...
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In this paper, a new approach to the harmonic estimation based on the linear transformation named Taylor-Fourier transform has been proposed. A multiple-resonator-based observer structure has been used. The output taps of the multiple resonators may fix not only the complex harmonic values but also, according to the actual resonator multiplicity, their first, second, third, fourth, and so on, derivatives at the corresponding frequency. The algorithm is recursive, which allows its implementation in embedded environment with limited memory. The estimation technique is suitable for application in a wide range of frequency changes, transient conditions, and interharmonics presence, with benefits in a reduced complexity and computational effort. To demonstrate the performance of the developed algorithm, computer simulated data records are processed.
The center problem at infinity is far to be solved in general. In this paper we develop a procedure to resolve it for a particular type of polynomial differential systems. The problem is solved by writing its concomit...
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The center problem at infinity is far to be solved in general. In this paper we develop a procedure to resolve it for a particular type of polynomial differential systems. The problem is solved by writing its concomitant differential equation in the complex coordinates introduced by Yirong Liu and by developing a new method of computation of the so called singular point quantities. This method is based on the transformation of infinity into the elementary origin. Finally, the investigation of center problem for the infinity of a particular family of planar polynomial vector fields of degree 5 is carried out to illustrate the main theoretical results. These involve extensive use of a Computer Algebra System, we have chosen to use Mathematica (R). (C) 2014 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.
The filtered-x recursive square(FxRLS) algorithm has been proven to be efficient for active noise control(ANC) systems. Unfortunately, its performance deteriorates when the ANC systems are corrupted by impulsive noise...
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The filtered-x recursive square(FxRLS) algorithm has been proven to be efficient for active noise control(ANC) systems. Unfortunately, its performance deteriorates when the ANC systems are corrupted by impulsive noises. To reduce this drawback, a novel filter-x recursive least M-estimate adaptive(Fx RLM) algorithm for ANC is proposed in this paper, which can overcome the adverse effect of impulsive noise on the adaptation of controller. Simulations in the context of ANC system show that the proposed Fx RLM algorithm outperforms the conventional Fx RLS, filtered-x least mean p-power(Fx LMP) and robust Fx LMS(RFx LMS) algorithms.
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