基于遗传算法的优化排样

介绍了遗传算法以及二维不规则图形的计算机优化排样，研究了二维不规则图形排样中如何建立遗传算法的目标函数，如何实现交叉、变异运算等优化过程，找到了运用遗传算法实现二维不规则图形优化排样的方法。通过计算机编制了运用遗传算法进行二维不规则图形优化排样的运算程序，并对部分样形进行了优化排样，结果比较满意。     

基于DTW算法的语音识别系统实现

动态时间归整(DTW)算法的实现简单有效,在孤立词语音识别系统中得到了广泛的应用.介绍了将DTW算法移植到TMS320VC5402上实现孤立词语音识别的原理、系统硬件组成和软件设计.研究结果表明,系统能满足实时性能要求,识别效果良好.     

基于滑窗结构的软输出维特比算法的实现

文章在对软输出维特比算法(SOVA)进行推导的基础上，分析了软信息的提取过程。同时从硬件实现的角度考虑，提出了一种基于滑动窗1：3结构的SOVA算法实现方案，该算法大大降低了算法实现复杂度和译码延迟，同时通过调整滑窗参数，可以取得与非滑窗SOVA算法几乎相同的性能。     

Adaptive Edge Detectors for Piecewise Smooth Data Based on the minmod Limiter

We are concerned with the detection of edges—the location and amplitudes of jump discontinuities of piecewise smooth data realized in terms of its discrete grid values. We discuss the interplay between two approaches. One approach, realized in the physical space, is based on local differences and is typically limited to low-order of accuracy. An alternative approach developed in our previous work [Gelb and Tadmor, Appl. Comp. Harmonic Anal., 7, 101–135 (1999)] and realized in the dual Fourier space, is based on concentration factors; with a proper choice of concentration factors one can achieve higher-orders—in fact in [Gelb and Tadmor, SIAM J. Numer. Anal., 38, 1389–1408 (2001)] we constructed exponentially accurate edge detectors. Since the stencil of these highly-accurate detectors is global, an outside threshold parameter is required to avoid oscillations in the immediate neighborhood of discontinuities. In this paper we introduce an adaptive edge detection procedure based on a cross-breading between the local and global detectors. This is achieved by using the minmod limiter to suppress spurious oscillations near discontinuities while retaining high-order accuracy away from the jumps. The resulting method provides a family of robust, parameter-free edge-detectors for piecewise smooth data. We conclude with a series of one- and two-dimensional simulations.To David Gottlieb, on his 60th birthday, with friendship and appreciation.     

EM procedures using mean field-like approximations for Markov model-based image segmentation

Image segmentation using Markov random fields involves parameter estimation in hidden Markov models for which the EM algorithm is widely used. In practice, difficulties arise due to the dependence structure in the models and approximations are required. Using ideas from the mean field approximation principle, we propose a class of EM-like algorithms in which the computation reduces to dealing with systems of independent variables. Within this class, the simulated field algorithm is a new stochastic algorithm which appears to be the most promising for its good performance and speed, on synthetic and real image experiments.     

Active set truncated-Newton algorithm for simultaneous optimization of distillation column

An active set truncated-Newton algorithm (ASTNA) is proposed to solve the large-scale bound constrained sub-problems. The global convergence of the algorithm is obtained and two groups of numerical experiments are made for the various large-scale problems of varying size. The comparison results between ASTNA and the subspace limited memory quasi-Newton algorithm and between the modified augmented Lagrange multiplier methods combined with ASTNA and the modified barrier function method show the stability and effectiveness of ASTNA for simultaneous optimization of distillation column.     

神经网络分类器的快速构造算法

使用ＢＰ算法训练多层网络的速度很慢而且事先难于确定隐节点和隐层的适当数目。本文提出一个有效的算法，先构造决策树，然后将构造的决策树转换为神经网。文中使用一个全局准则函数控制决策树的增长，它较好地匹配了树的复杂性和训练样本量及错分率界。实验结果，本文的算法比用ＢＰ算法训练多层网络要快，而其分类精度不低于用ＢＰ算法训练的多层神经网。     

一种产生式系统的并行推理模型

1.引言 在人工智能领域中,产生式系统是一种比较有效的知识表示方法,并得到了广泛应用。目前许多较为成功的专家系统都是用产生式系统实现的~[1]。随着应用领域的不断扩士其知识座的抓橄称渐增     

红外目标精跟踪方法的研究

本文提出了一种直接把红外前视传感器的输出作为测量值来跟踪红外目标的扩展型卡尔曼滤波跟踪算法。在滤波处理时,设置可变的测量矩阵,其目的在于提高运算速度和抑制噪声干扰。通过具体的实例,对所提跟踪算法的特点和有效性进行了细致的分析。