面向服务的分布式IDS自组织模型研究

本文将面向服务的思想引入分布式入侵检测系统,提出了一种面向服务的分布式IDS自组织模型--SODIDS。该模型使用基于索引的多层P2P网络来发布和查找安全服务信息,通过快速检索安全服务信息,各种安全部件相互协作,自组织成为分布式入侵检测系统,实现了多域合作、自动配置。     

基于汉字与部件聚类的计算机模拟研究

以自组织特征映射算法为理论基础，提出了一种新的SOMNET算法，利用人工神经网络实现了具有相似特征的汉字的聚类以及汉字部件的聚类。并对汉字及其部件的显示效果进行了分析，从不同角度刻画了SOM模型的应用。研究结果对于汉字认知研究以及留学生第二语言习得教学具有一定的参考价值。     

Developments of fuzzy PID controllers

Abstract: This paper describes the development and tuning methods for a novel self-organizing fuzzy proportional integral derivative (PID) controller. Before applying fuzzy logic, the PID gains are tuned using a conventional tuning method. At supervisory level, fuzzy logic readjusts the PID gains online. In the first tuning method, fuzzy logic at the supervisory level readjusts the three PID gains during the system operation. In the second tuning method, fuzzy logic only readjusts the proportional PID gain, and the corresponding integral and derivative gains are readjusted using the Ziegler–Nichols tuning method while the system is in operation. For the compositional rule of inferences in the fuzzy PID and the self-organizing fuzzy PID schemes two new approaches are introduced: the min implication function with the mean of maxima defuzzification method, and the max-product implication function with the centre of gravity defuzzification method. The fuzzy PID controller, the self-organizing fuzzy PID controller and the PID controller are all applied to a non-linear revolute-joint robot arm for step input and path tracking experiments using computer simulation. For the step input and path tracking experiments, the novel self-organizing fuzzy PID controller produces a better output response than the fuzzy PID controller; and in turn both controllers exhibit better process output than the PID controller.     

A New Methodology for Efficient Classification of Multispectral Satellite Images Using Neural Network Techniques

A methodology based on self-organizing feature maps and indexing techniques for time and memory efficient neural network training and classification of large volumes of remotely sensed data is presented. Results on land-cover classification of multispectral satellite images using two popular neural models show orders of magnitude of speedup with respect to both training and classification times. The generality of the proposed methodology is demonstrated with a dramatic improvement of the classification time of the k-nearest neighbors statistical classifier.     

Neural navigation interfaces for Information Retrieval: Are they more than an appealing idea?

Neural networks have recently been proposed for the construction of navigation interfaces for Information Retrieval systems. In this paper, we give an overview of some current research in this area. Most of the cited approaches use (variants) of the well-known Kohonen network. The Kohonen network implements a topology-preserving dimensionality-reducing mapping, which can be applied for information visualization. We identify a number of problems in the application of Kohonen networks for Information Retrieval, most notably scalability, reliability and retrieval effectiveness. To solve these problems we propose to use the Growing Cell Structures network, a variant of the Kohonen network which shows a more flexible adaptation to the domain structure.This network was tested on two standard test-collections, using a combined recall and precision measure, and compared to traditional IR methods such as the Vector Space Model and various clustering algorithms. The network performs at a competitive level of effectiveness, and is suitable for visualization purposes. However, the incremental training procedures for the networks result in a reliability problem, and the approach is computationally intensive. Also, the utility of the resulting maps for navigation will need further improvement.     

基于神经网络和线性像素置换的颜色量化

本文提出了一种新的彩色图像量化算法.它是一种基于自组织神经网络和线性像素置换的后聚类算法.线性像素置换是一种均匀选取图像中的像素的方法.根据线性像素置换确定改进的自组织神经网络的初始权重向量和训练样本集.选取部分样本参加训练加快训练过程.实验结果表明,与其它量化优化算法比较,本文提出的算法在量化图像质量和算法效率方面均有明显提高,而且不依赖于算法的初始条件.     

层次匹配范例推理在短期负荷预测中的应用

提出了一个改进的范例推理系统来解决电力系统短期负荷预测问题,该系统将范例推理、自组织映射以及模糊粗糙集方法进行了有效的结合.使用模糊粗糙集方法确定了范例的表示、组织方法，并通过自组织映射对历史范例进行聚类.将新问题所对应的范例与各个聚类中心进行匹配，得到最相似聚类，再在该聚类中进行二次匹配，对得到的最相似范例集进行重用、修正，从而得到最终预测结果.使用模糊粗糙集方法可以进行范例属性和匹配权重的合理选择，同时使用自组织映射对历史范例进行聚类，可以减少范例匹配次数和匹配时间.使用该方法不仅可以合理利用历史范例，而且可以通过属性选取、聚类来获取附加知识.实例验证和比较结果表明该负荷预测方法是有效可行的.     

Surface Structure Etching Law of Self-Organizing Nanomaterials Based on AFM and SEM

Abstract

With the rapid development of nanotechnology, more and more products need to be etched on the surface of nanomaterials. Due to its own limitations, it is difficult to obtain in-depth and effective data results. In order to solve the research problem of etching law, this paper studies the surface structure etching law of self-organized nanomaterials by using AFM and SEM, and analyzes its characteristics in detail, and draws the following conclusions: through the use of AFM and SEM, the needles were coated with different forces, etching speeds, and cycles of etching to etch self-organized nanomaterials. The etching depth increased with the increase of etching force. The results show that the self-organized nanomaterials in this paper are suitable for regular etching. Processing; by changing the force parameters applied by the probe, the speed of the probe etch, and the number of cycles of the probe, the etching effect of the diamond-like film coated probe on the P-Si(100) wafer was found. As the etching force increases (1?μN-12?μN), the depth and width of the etched self-organized nanomaterial gradually increase, but as the etching force continues to increase (12?μN-20μN), the etch depth appears saturated. The etch rate has little effect on the etch depth and width.     

Neurobiology suggests the design of modular architectures for neural control

The existence of modular structures in the biological world strongly suggests that the training of this kind of structure is actually feasible. It is a key indication for the development of neural network applications, especially in the field of robotics. Indeed, a single network can only efficiently treat problems with few independent variables; the combination of several networks is necessary to address more complex tasks. We investigate learning techniques and show that using a particular form of architecture can ease the training of a modular structure: a bi-directional structure that allows combining several neural networks. The approach is illustrated with Kohonen's self-organizing maps for a robotic visual servoing task.     

THE PHASE WEB PARADIGM

We describe an information-oriented, computational physical theory based on expressing knowledge of the world via patterns of synchronization. The systems this theory describes are self-organizing, display emergent phenomena, and can be expressed in terms both of Clifford algebras and algebraic topology, and practical running programs.