关于最佳鉴别特征维数问题的讨论

该文对最佳鉴别特征的最佳维数问题进行了详细的讨论．文章首先对最佳维数问题进行了界定，然后指出了两种最佳特征维数为c-1维的情况即以某些基于矩的可分性判据(准则函数)为优化目标的最优特征和以某些特殊的分类器错误率为优化目标的最优特征．最后该文运用方差分析法对最佳鉴别特征进行特征选择使之代入最小距离分类器后识别率最大．     

基于混沌序列的多重数字图像水印算法

采用一维混沌映射，根据不同的混沌初始条件生成多个互不相关的混沌实值序列，进而得到性能良好的多重数字图像水印信号，有效地解决了多重数字图像水印信号的构造问题．同时采用二维混沌映射将各水印信号置乱，随机均匀地叠加在图像小波变换的中低频系数上，各水印信号的嵌入和提取可以互不干扰，解决了多重数字图像水印的嵌入问题．实验结果表明，该算法对各种常见的攻击均具备了一定的抵抗能力，可有效地解决数字图像产品的多著作权问题．     

用遗传BP网络进行图像边缘检测

该文提出了一种基于遗传算法与图像特征向量的边缘检测方法。由于噪声的干扰,常规的图像边缘检测方法往往效果不佳,因此在充分考虑边缘和噪声本质区别的基础上,构造具有较强抗噪能力的特征向量;然后用样本图像对多层前馈神经网络采用遗传学习算法和误差反向传播算法(BP)相结合进行训练,即先用遗传学习算法进行全局训练,再用BP算法进行精确训练,使网络收敛速度加快和避免局部极小。最后,将训练后的网络用于图像的边缘检测。实验证明这种方法是有效的。     

Application of multi-layer perceptron neural networks to vision problems

This paper discusses the application of a class of feed-forward Artificial Neural Networks (ANNs) known as Multi-Layer Perceptrons(MLPs) to two vision problems: recognition and pose estimation of 3D objects from a single 2D perspective view; and handwritten digit recognition. In both cases, a multi-MLP classification scheme is developed that combines the decisions of several classifiers. These classifiers operate on the same feature set for the 3D recognition problem whereas different feature types are used for the handwritten digit recognition. The backpropagationlearning rule is used to train the MLPs. Application of the MLP architecture to other vision problems is also briefly discussed.     

Handwritten Chinese Character Recognition Based on Primitive and Fuzzy Features via the SEART Neural Net Model

A handwritten Chinese character recognition method based on primitive and compound fuzzy features using the SEART neural network model is proposed. The primitive features are extracted in local and global view. Since handwritten Chinese characters vary a great deal, the fuzzy concept is used to extract the compound features in structural view. We combine the two categories of features and use a fast classifier, called the Supervised Extended ART (SEART) neural network model, to recognize handwritten Chinese characters. The SEART classifier has excellent performance, is fast, and has good generalization and exception handling abilities in complex problems. Using the fuzzy set theory in feature extraction and the neural network model as a classifier is helpful for reducing distortions, noise and variations. In spite of the poor thinning, a 90.24% recognition rate on average for the 605 test character categories was obtained. The database used is CCL/HCCR3 (provided by CCL, ITRI, Taiwan). The experiment not only confirms the feasibility of the proposed system, but also suggests that applying the fuzzy set theory and neural networks to recognition of handwritten Chinese characters is an efficient and promising approach.     

基于混沌理论的交通量实时预测

分析了城市交通的混沌性，根据复杂的城市交通特点，引入了误差反馈系数，改进了混沌时间序列预测方法中的加权一阶局域法和基于最大Lyapunov指数的预测法，并将其成功应用于实时交通量预测．预测结果表明：这两种改进的方法都能较准确地预测交通量，但后者比前者更适合交通量预测，后者的预测误差一般可以控制在5％以下．     

基于统一混沌系统的广义同步

利用广义同步的方法 ,研究了统一混沌系统的同步控制 .使用混沌遮掩的方法进行了数值仿真实验 .结果证明了统一混沌系统广义同步的有效性 指出了其应用前景.     

统一混沌系统的脉冲控制与同步

对统一混沌系统的脉冲控制稳定性与同步问题进行了理论分析，利用脉冲微分方程理论给出了脉冲控制稳定与同步的充分条件，并对脉冲控制时间间隔的范围作了估计。最后，通过仿真进行了验证。     

Evaluation of Linguistic Features for Word Sense Disambiguation with Self-Organized Document Maps

Word sense disambiguation automatically determines the appropriate senses of a word in context. We have previously shown that self-organized document maps have properties similar to a large-scale semantic structure that is useful for word sense disambiguation. This work evaluates the impact of different linguistic features on self-organized document maps for word sense disambiguation. The features evaluated are various qualitative features, e.g. part-of-speech and syntactic labels, and quantitative features, e.g. cut-off levels for word frequency. It is shown that linguistic features help make contextual information explicit. If the training corpus is large even contextually weak features, such as base forms, will act in concert to produce sense distinctions in a statistically significant way. However, the most important features are syntactic dependency relations and base forms annotated with part of speech or syntactic labels. We achieve 62.9% ± 0.73% correct results on the fine grained lexical task of the English SENSEVAL-2 data. On the 96.7% of the test cases which need no back-off to the most frequent sense we achieve 65.7% correct results.