基于RBF网络的混沌动力系统辨识

提出用RBF神经网络对混沌动力系统进行辨识，设计了一个三层RBF网络结构，仿真实验说明了RBF网络用于学习混沌动力系统时的基本性质。用辨识模型重建吸引子方法定性地评价辨识模型，通过计算辨识模型的Lyapunov指数定量地评价辨识模型的性能，同时推导了RBF网络模型Lyapunov指数的计算公式。仿真结果表明，该辨识模型能很好地逼近原混沌动力系统，准确地体现原混沌系统的动力学特性。     

传统扩频与混沌扩频的CDMA系统在Rappaport Indoor信道下的性能比较

 对Logistic映射的混沌序列作为扩频序列构成的混沌扩频CDMA系统与传统的m序列扩频CDMA系统进行了对比研究,并且对其二者在Rappaport Indoor信道下的性能在SPW上进行了仿真、分析,具有重要实际意义.     

采用混沌扩频序列的异步码分多址通信系统仿真

为了获取适用于异步码分多址系统的混沌序列 ,提出了一种基于扩频序列性能分析准则的优选方法 ,并用系统仿真的方法进行了验证。通过对异步码分多址系统的性能分析 ,总结了扩频序列的性能准则 ,以此为依据对初值不同的混沌序列进行筛选 ,可以得到性能优异的混沌扩频序列。采用 Monte Carlo的方法 ,在无线衰落信道下对采用混沌序列和 Gold序列的异步码分多址系统进行了仿真。结果表明 :优选的混沌序列性能要优于 Gold序列 ,是扩频序列的一种新的选择     

采用时空混沌耦合映象格子产生混沌扩频序列

利用耦合映象格子这一具体的时空混沌模型来产生混沌扩频序列，研究了产生混沌扩频序列的方法，并对序列特性进行了分析，结果证明产生出来的序列扩频特性性能良好，可以应用于扩频通信系统。     

A genetic algorithm on multiple sequences alignment problems in biology

The study and comparison of sequences of characters from a finite alphabet is relevant to various areas of science, notably molecular biology. The measurement of sequence similarity involves the consideration of the possible sequence alignments in order to find an optimal one for which the “distance” between sequences is minimum. In biology informatics area, it is a more important and difficult problem due to the long length (100 at least) of sequence, this cause the compute complexity and large memory require. By associating a path in a lattice to each alignment, a geometric insight can be brought into the problem of finding an optimal alignment, this give an obvious encoding of each path. This problem can be solved by applying genetic algorithm, which is more efficient than dynamic programming and hidden Markov model using commomly now. Foundation item: Supported by Zi-qiang Foundation of Wuhan University and Open Foundation of the State Key-Laboratory of Software Engineering, Wuhan University Biography: Shi Feng(1966-), male, Associate professor, research direction: bioinformatics.     

一种基于混沌迭代的加密算法

提出一种基于混沌迭代的加密算法，这种加密算法根据混沌迭代确定的位置将明文填入一个较大的密文空间。反之，利用混沌迭供的确定性，可以方便地从密文空间中取出密文单元，重新排列得到明文。     

数字混沌加密算法的某些改进算法

对文献［１］提出的数字混沌加密算法作了某些分析，证明了Ｆｒｅｙ提出的数字混沌加密算法的解是唯一的，因而是可以应用的。讨论了工程应用中的某些约束条件，并在原来算法的基础上给出了能引入分叉的改进算法，从而使序列更为复杂。     

Spectral analysis of phylogenetic data

The spectral analysis of sequence and distance data is a new approach to phylogenetic analysis. For two-state character sequences, the character values at a given site split the set of taxa into two subsets, a bipartition of the taxa set. The vector which counts the relative numbers of each of these bipartitions over all sites is called a sequence spectrum. Applying a transformation called a Hadamard conjugation, the sequence spectrum is transformed to the conjugate spectrum. This conjugation corrects for unobserved changes in the data, independently from the choice of phylogenetic tree. For any given phylogenetic tree with edge weights (probabilities of state change), we define a corresponding tree spectrum. The selection of a weighted phylogenetic tree from the given sequence data is made by matching the conjugate spectrum with a tree spectrum. We develop an optimality selection procedure using a least squares best fit, to find the phylogenetic tree whose tree spectrum most closely matches the conjugate spectrum. An inferred sequence spectrum can be derived from the selected tree spectrum using the inverse Hadamard conjugation to allow a comparison with the original sequence spectrum. A possible adaptation for the analysis of four-state character sequences with unequal frequencies is considered. A corresponding spectral analysis for distance data is also introduced. These analyses are illustrated with biological examples for both distance and sequence data. Spectral analysis using the Fast Hadamard transform allows optimal trees to be found for at least 20 taxa and perhaps for up to 30 taxa. The development presented here is self contained, although some mathematical proofs available elsewhere have been omitted. The analysis of sequence data is based on methods reported earlier, but the terminology and the application to distance data are new.     

独立随机变量第k个最大值的密度函数的局部一致收敛

ξ_1,ξ_2,…,ξ_n是独立同分布随机变量,公共分布函数F(x)绝对连续,g_n.k(x)为ξ_1,…,ξ_n的第k个规范化最大值的分布密度函数.本文讨论了g_n,k(x)的局部一致收敛性以及在L_p(O相似文献