基于Kohonen神经网络聚类方法在遥感分类中的比较

设计完成和比较了基于Kohonen自组织网络的Kohonen聚类网络（Kohonen Clustering Network, KCN）、模糊Kohonen聚类网络（Fluzzy KCN, FKCN）和基于进化规划的Kohonen聚类网络（Evalutionary Programming based KCN, EPKCN）三种聚类算法在遥感土地利用/覆盖分类中的应用。结果表明三种非监督学习方法在进行遥感土地利用/覆盖分类过程中，在分类性能上有显著差异。EPKCN分类目视效果最好，单次迭代的速度最快；FKCN总的收敛速度最快；而按遥感土地利用/覆盖分类要求而言，EPKCN方法在三种分类方法中效果最好，因此可采用该算法进行遥感土地利用/覆盖的非参数分类。     

矿业软件在矿产储量评价中的应用

矿产储量评价的基本内容是建立矿床地质模型进行品位估计和储量分类分级．地质控制条件限制是矿床建模的重要手段。地质统计学方法是矿块品位估计以及储量分级等的有效方法．国内的应用实例表明。国际先进矿业软件是矿产储量评价的先进工具，应用矿业软件进行矿产储量评价是市场经济的需要。     

Classification of heart rate data using artificial neural network and fuzzy equivalence relation

The electrocardiogram is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks, etc. may contain useful information about the nature of disease afflicting the heart. However, these subtle details cannot be directly monitored by the human observer. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the signal parameters, extracted and analysed using computers, are highly useful in diagnostics. This paper deals with the classification of certain diseases using artificial neural network (ANN) and fuzzy equivalence relations. The heart rate variability is used as the base signal from which certain parameters are extracted and presented to the ANN for classification. The same data is also used for fuzzy equivalence classifier. The feedforward architecture ANN classifier is seen to be correct in about 85% of the test cases, and the fuzzy classifier yields correct classification in over 90% of the cases.     

小波网络在电力系统故障信号处理中的应用研究

从小波分析中对函数逼近表示的不同角度，分别介绍了3种主要的小波网络，并对这3种小波网络的构成、网络模型和学习算法等进行了详细介绍和比较，给出了它们之间的本质区别。在此基础之上，对小波网络在电力系统故障信号分类和故障数据压缩方面的应用进行了讨论，给出的相应数字仿真结果表明，小波网络在电力系统故障信号处理方面的应用是完全可行的。     

再生核空间 H_0~1[a,b]中样条插值算子

讨论了再生核空间Ｈ１０［ａ，ｂ］中，样条插值算子、分段再生核插值算子及最佳插值逼近算子的一致性．这一结果，不仅深化了对再生核空间中插值逼近问题的研究，而且应用这３种算子可相互取长补短，灵活运用，为数值计算提供了方便．     

Polarization Phase-Based Method For Material Classification In Computer Vision

A robust and accurate polarization phase-based technique for material classification is presented. The novelty of this technique is three-fold in (i) its theoretical development, (ii) application, and, (iii) experimental implementation. The concept of phase of polarization of a light wave is introduced to computer vision for discrimination between materials according to their intrinsic electrical conductivity, such as distinguishing conducting metals, and poorly conducting dielectrics. Previous work has used intensity, color and polarization component ratios. This new method is based on the physical principle that metals retard orthogonal components of light upon reflection while dielectrics do not. This method has significant complementary advantages with respect to existing techniques, is computationally efficient, and can be easily implemented with existing imaging technology. Experiments for real circuit board inspection, nonconductive and conductive glass, and, outdoor object recognition have been performed to demonstrate its accuracy and potential capabilities.     

A Neuro-fuzzy Coding for Processing Incomplete Data: Application to the Classification of Seismic Events

This letter presents a method for modelling and processing incomplete data in connectionist systems. The approach consists in applying a neuro-fuzzy coding to the input data of a neural network. After an introduction to the different kinds of imperfections, we propose a neuro-fuzzy coding in order to take incomplete data into account. We show the efficiency of this coding on the problem of the classification of seismic events. The results show that a neuro-fuzzy coding of the inputs of a neural network increases the performance and classifies incomplete data with little affect on the results.     

一种融合PCA 和KFDA 的人脸识别方法

提出一种融合PCA和KFDA的人脸识别方法，即在进行非线性映射之前，首先利用经典的主分量分析(C—PCA)进行降维，然后执行KFDA．为进一步降低整个算法的计算时问，又提出一种I—PCA KFDA方法，它直接基于图像矩阵的主分量分析(I—PCA)．ORL标准人脸库的试验结果表明，与现有的核Fisher鉴别分析方法相比，两种方法可将特征抽取的速度分别提高3倍和7倍，其识别精度没有丝毫的降低．     

模糊核聚类的自适应算法

针对模糊聚类算法在样本特征不明显时不能取得很好的聚类效果，以及现有的模糊聚类算法需要事先确定聚类数，随机性强、容易陷入局部最优等弱点，将核函数和有效性函数引入到模糊聚类中，提出了模糊核聚类的自适应算法,此方法在性能上比经典的聚类算法有了较大的改进,取得了更好的聚类效果,实验结果证实了该方法的有效性和可行性.     

在World Wide Web上检索图像和视频信息

提出了一种综合利用图像和视频的文本信息和基于内容的视觉特征进行World Wide Web上图像视频信息检索的原型系统。在这个原型系统中，一个完整的Web上图像和视频信息的处理过程包括：(1)通过探索器从Web上自动收集信息；(2)在文本和视频特征两个域内同时进行分析；(3)图像和视频信息分类；(4)标定索引，进行快速检索。实验结果表明，利用这个系统，可以获得较高的视觉分类率。