With the developing of the microprocessor and SOC (system on a chip) techniques, the research on the testability of mixed-signal (digital and analogue signal) circuits has became urgently requisite. The Discrete Event...
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ISBN:
(纸本)078037925X
With the developing of the microprocessor and SOC (system on a chip) techniques, the research on the testability of mixed-signal (digital and analogue signal) circuits has became urgently requisite. The Discrete Event System (DES) theory gave a uniform and systematic approach for the testability and faulty-diagnosis problems of mixed-signal circuits. In this approach, one of the two main tasks is finding the minimum test set of the mixed-signal circuits. This paper illustrates a combinational optimization method based on the Simulated Annealing for the minimum test set of the mixed-signal circuits. Then there is the discussion about the convergence the algorithm's searching process. Finally, some research directions are pointed out.
A new global optimization algorithm is presented in IC reliability simulator *** dominant factor is introduction of NTM(number-theoretic method) and combination of the weighted centroid,the we
ISBN:
(纸本)0780365208
A new global optimization algorithm is presented in IC reliability simulator *** dominant factor is introduction of NTM(number-theoretic method) and combination of the weighted centroid,the we
To solve a system of nonlinear equations, Wu wen-tsun introduced a new formative elimination method. Based on Wu's method and the theory of nonlinear programming, we here propose a global optimization algorithm fo...
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To solve a system of nonlinear equations, Wu wen-tsun introduced a new formative elimination method. Based on Wu's method and the theory of nonlinear programming, we here propose a global optimization algorithm for nonlinear programming with rational objective function and rational constraints. The algorithm is already programmed and the test results are satisfactory with respect to precision and reliability.
A significant multi-stage financial planning problem is posed as a stochastic program with decision rules. The decision rule - called dynamically balanced - requires the purchase and sale of assets at each time stage ...
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A significant multi-stage financial planning problem is posed as a stochastic program with decision rules. The decision rule - called dynamically balanced - requires the purchase and sale of assets at each time stage so as to keep constant asset proportions in the portfolio composition. It leads to a nonconvex objective function. We show that the rule performs well as compared with other dynamic investment strategies. We specialize a global optimization algorithm for this problem class - guaranteeing finite E-optimal convergence. Computational results demonstrate the procedure's efficiency on a real-world financial planning problem. The tests confirm that local optimizers are prone to erroneously underestimate the efficient frontier. The concepts can be readily extended for other classes of long-term investment strategies.
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