Dembowski-Ostrom型完全非线性函数构造的线性码权分布

Dembowski-Ostrom型完全非线性函数是目前最主要的完全非线性函数类,已发现的完全非线性函数中只有一种不属于Dembowski-Ostrom型.为此,该文首先给出Dembowski-Ostrom型完全非线性函数的定义,将已有的线性码构造推广到这一类型函数上.进而给出此类函数构造的线性码的码字与有限域上非退化二次型之间的关系,并得到相应二次型的原像分布的一些性质.通过有限域上的二次型以及指数和理论,用统一的方法完全确定了基于所有Dembowski-Ostrom型完全非线性函数构造的两类线性码的权分布.

Optimal bounded control of first-passage failure of strongly non-linear oscillators under combined harmonic and white-noise excitations

A procedure for designing the optimal bounded control of strongly non-linear oscillators under combined harmonic and white-noise excitations for minimizing their first-passage failure is proposed. First, a stochastic averaging method for strongly non-linear oscillators under combined harmonic and white-noise excitations using generalized harmonic functions is introduced. Then, the dynamical programming equations and their boundary and final time conditions for the control problems of maximizing reliability and of maximizing mean first-passage time are formulated from the averaged Ito equations by using the dynamical programming principle. The optimal control law is derived from the dynamical programming equations and control constraint. Finally, the conditional reliability function, the conditional probability density and mean of the first-passage time of the optimally controlled system are obtained from solving the backward Kolmogorov equation and Pontryagin equation. An example is given to illustrate the proposed procedure and the results obtained are verified by using those from digital simulation. (C) 2003 Elsevier Ltd. All rights reserved.

Direct dynamical test for deterministic chaos and optimal embedding of a chaotic time-series

We propose here a local exponential divergence plot which is capable of providing an alternative means of characterizing a complex time series. The suggested plot defines a time-dependent exponent and a ''plus'' exponent. Based on their changes with the embedding dimension and delay time, a criterion for estimating simultaneously the minimal acceptable embedding dimension, the proper delay time, and the largest Lyapunov exponent has been obtained. When redefining the time-dependent exponent LAMBDA(k) curves on a series of shells, we have found that whether a linear envelope to the LAMBDA(k) curves exists can serve as a direct dynamical method of distinguishing chaos from noise.

Optimal design of a 7 T highly homogeneous superconducting magnet for a Penning trap

A Penning trap system called Lanzhou Penning Trap (LPT) is now being developed for precise mass measurements at the Institute of Modern Physics (IMP). One of the key components is a 7 T actively shielded superconducting magnet with a clear warm bore of 156 mm. The required field homogeneity is 3 x 10(-7) over two 1 cubic centimeter volumes lying 220 mm apart along the magnet axis. We introduce a two-step method which combines linear programming and a nonlinear optimization algorithm for designing the multi-section superconducting magnet. This method is fast and flexible for handling arbitrary shaped homogeneous volumes and coils. With the help of this method an optimal design for the LPT superconducting magnet has been obtained.

Algorithm Studies on How to Obtain a Conditional Nonlinear Optimal Perturbation (CNOP)

The conditional nonlinear optimal perturbation (CNOP), which is a nonlinear generalization of the linear singular vector (LSV), is applied in important problems of atmospheric and oceanic sciences, including ENSO predictability, targeted observations, and ensemble forecast. In this study, we investigate the computational cost of obtaining the CNOP by several methods. Differences and similarities, in terms of the computational error and cost in obtaining the CNOP, are compared among the sequential quadratic programming (SQP) algorithm, the limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm, and the spectral projected gradients (SPG2) algorithm. A theoretical grassland ecosystem model and the classical Lorenz model are used as examples. Numerical results demonstrate that the computational error is acceptable with all three algorithms. The computational cost to obtain the CNOP is reduced by using the SQP algorithm. The experimental results also reveal that the L-BFGS algorithm is the most effective algorithm among the three optimization algorithms for obtaining the CNOP. The numerical results suggest a new approach and algorithm for obtaining the CNOP for a large-scale optimization problem.