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Efficient algorithms are described for computing the probability of detection for a binary integration when the probability of a threshold crossing changes from sample to sample. A binary integrator is exceeded (a hit occurs) in a...
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Efficient algorithms are described for computing the probability of detection for a binary integration when the probability of a threshold crossing changes from sample to sample. A binary integrator is exceeded (a hit occurs) in a sequence of N trials and declares a detection if the number of hits is at least as large as some number M, where O>or=M>or=N. J.S. Brunner (1990) described an efficient iterative method for the computation of these probabilities. The author gives an improved version of Brunner's algorithm, shows how to compute the probability of detection directly, and how to avoid needless computation when the probability of detection needs to be determined for one M only.
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This paper deals with a general type of linear matrix equation problem. It presents new iterative algorithms to solve the matrix equations of the form A_iXB_i = F_i. These algorithms are based on the incremental subgradient and th...
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This paper deals with a general type of linear matrix equation problem. It presents new iterative algorithms to solve the matrix equations of the form A_iXB_i = F_i. These algorithms are based on the incremental subgradient and the parallel subgradient methods. The convergence region of these algorithms are larger than other existing iterative algorithms. Finally, some experimental results are presented to show the efficiency of the proposed algorithms.
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In the above article [1] , the inequality in (12) should be \begin{equation*} \text {gcd}(\delta _{1},\delta _{2},\ldots,\delta _{N},2\pi) 2\pi /(K+1)\end{equation*} rather than \begin{equation*} \text {gcd}(\delta _{1},\de...
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In the above article [1] , the inequality in (12) should be \begin{equation*} \text {gcd}(\delta _{1},\delta _{2},\ldots,\delta _{N},2\pi) 2\pi /(K+1)\end{equation*} rather than \begin{equation*} \text {gcd}(\delta _{1},\delta _{2},\ldots,\delta _{N},2\pi) 2\pi /K.\end{equation*}
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The polynomial iteration algorithm to realize the robust parametric and structural estimation within the frame of GMDH technique is presented. The two-level neural network structure with the controlled model complexity provides th...
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The polynomial iteration algorithm to realize the robust parametric and structural estimation within the frame of GMDH technique is presented. The two-level neural network structure with the controlled model complexity provides the computational stability of GMDH-PNN algorithm. The computational experiment demonstrating the parametric and structural robustness in the presence of outliers as well as examples of modeling problems solution in pharmacology and medicine are described.
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Aimed at infinite horizon optimal control problems of discrete time-varying nonlinear systems, in this paper, a new iterative adaptive dynamic programming algorithm, which is the discrete-time time-varying policy iteration (DTTV) ...
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Aimed at infinite horizon optimal control problems of discrete time-varying nonlinear systems, in this paper, a new iterative adaptive dynamic programming algorithm, which is the discrete-time time-varying policy iteration (DTTV) algorithm, is developed. The iterative control law is designed to update the iterative value function which approximates the index function of optimal performance. The admissibility of the iterative control law is analyzed. The results show that the iterative value function is non-increasingly convergent to the Bellman-equation optimal solution. To implement the algorithm, neural networks are employed and a new implementation structure is established, which avoids solving the generalized Bellman equation in each iteration. Finally, the optimal control laws for torsional pendulum and inverted pendulum systems are obtained by using the DTTV policy iteration algorithm, where the mass and pendulum bar length are permitted to be time-varying parameters. The effectiveness of the developed method is illustrated by numerical results and comparisons.
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Solving non-linear equation is perhaps one of the most difficult problems in all of numerical computations, especially in a diverse range of engineering applications. The convergence and performance characteristics can be highly s...
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Solving non-linear equation is perhaps one of the most difficult problems in all of numerical computations, especially in a diverse range of engineering applications. The convergence and performance characteristics can be highly sensitive to the initial guess of the solution for most numerical methods such as Newton’s method. However, it is very difficult to select reasonable initial guess of the solution for most systems of non-linear equations. Besides, the computational efficiency is not high enough. Taking this into account, based on variational iteration technique, we develop some new iterative algorithms for solving one-dimensional non-linear equations. The convergence criteria of these iterative algorithms has also been discussed. The superiority of the proposed iterative algorithms is illustrated by solving some test examples and comparing them with other well-known existing iterative algorithms in literature. In the end, the graphical comparison of the proposed iterative algorithms with other well-known iterative algorithms have been made by means of polynomiographs of different complex polynomials which reflect the fractal behavior and dynamical aspects of the proposed iterative algorithms.
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We propose an iterative algorithm for solving the reflexive solution of the quaternion matrix equation AXB+CXHD=F. When the matrix equation is consistent over reflexive matrix X, a reflexive solution can be obtained within finite ...
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We propose an iterative algorithm for solving the reflexive solution of the quaternion matrix equation AXB+CXHD=F. When the matrix equation is consistent over reflexive matrix X, a reflexive solution can be obtained within finite iteration steps in the absence of roundoff errors. By the proposed iterative algorithm, the least Frobenius norm reflexive solution of the matrix equation can be derived when an appropriate initial iterative matrix is chosen. Furthermore, the optimal approximate reflexive solution to a given reflexive matrix X0 can be derived by finding the least Frobenius norm reflexive solution of a new corresponding quaternion matrix equation. Finally, two numerical examples are given to illustrate the efficiency of the proposed methods.
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Themanagement of a car-rental service becomesmore complex as long as one-way bookings between different depots are accepted. These bookings can increase the operational costs due to the necessity ofmoving vehicles fromone depot to...
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Themanagement of a car-rental service becomesmore complex as long as one-way bookings between different depots are accepted. These bookings can increase the operational costs due to the necessity ofmoving vehicles fromone depot to another by the company staff in order to attend previously accepted bookings.We present an iterative model based on flows on networks for the acceptance of bookings by a car-rental service that permits one-way reservations. Our model lets us also recover the movement of the fleet of vehicles between the depots over the time. In addition, it also permits including restrictions on the amount of cars managed at every single depot.These results can be of interest for an electric car-rental service that operates at different depots within a city or region.
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We introduce and study a class of new general systems of set-valued variational inclusions involving (A, η)-maximal relaxed monotone operators in Hilbert spaces. By using the general resolvent operator technique associated with (...
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We introduce and study a class of new general systems of set-valued variational inclusions involving (A, η)-maximal relaxed monotone operators in Hilbert spaces. By using the general resolvent operator technique associated with (A, η)-maximal relaxed monotone operators, we construct some new iterative algorithms for finding approximation solutions to the general system of setvalued variational inclusion problem and prove the convergence of this algorithm. Our results improve and extend some known results.
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In this study, we have introduced a framework for an automatic patient registration procedure using freely distributed fiducial markers within a robot application in neurosurgery. The localization procedures in the image space and...
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In this study, we have introduced a framework for an automatic patient registration procedure using freely distributed fiducial markers within a robot application in neurosurgery. The localization procedures in the image space and in the physical space are fully automated. We have developed a novel algorithm for finding the point pair correspondence between freely distributed fiducial markers in the image and in the physical space. The algorithm introduces a similarity matrix to maximize the possibility of successful point pairing and to remove the potential outlier points. The correspondence algorithm has been tested in 900,000 computer simulations and also on the real data from five laboratory phantom CT scans and twelve clinical patient CT scans, which were paired with 1415 readings captured with an optical tracking system. Testing of simulated point scenarios showed that the correspondence algorithm has a higher percentage of success when a larger number of fiducial markers and a lower number of outlier points were present. In the 24055 tests on the clinical data, there has been a 100 % success rate.
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