Knowledge discovery standards

As knowledge discovery (KD) matures and enters the mainstream, there is an onus on the technology developers to provide the technology in a deployable, embeddable form. This transition from a stand-alone technology, in the control of the knowledgeable few, to a widely accessible and usable technology will require the development of standards. These standards need to be designed to address various aspects of KD ranging from the actual process of applying the technology in a business environment, so as to make the process more transparent and repeatable, through to the representation of knowledge generated and the support for application developers. The large variety of data and model formats that researchers and practitioners have to deal with and the lack of procedural support in KD have prompted a number of standardization efforts in recent years, led by industry and supported by the KD community at large. This paper provides an overview of the most prominent of these standards and highlights how they relate to each other using some example applications of these standards.     

Knowledge representation for mathematical discovery: Three experiments in graph theory

This paper describes the nature of mathematical discovery (including concept definition and exploration, example generation, and theorem conjecture and proof), and considers how such an intelligent process can be simulated by a machine. Although the material is drawn primarily from graph theory, the results are immediately relevant to research in mathematical discovery and learning.The thought experiment, a protocol paradigm for the empirical study of mathematical discovery, highlights behavioral objectives for machine simulation. This thought experiment provides an insightful account of the discovery process, motivates a framework for describing mathematical knowledge in terms of object classes, and is a rich source of advice on the design of a system to perform discovery in graph theory. The evaluation criteria for a discovery system, it is argued, must include both a set of behavior to display (behavioral objectives) and a target set of facts to be discovered (factual knowledge).Cues from the thought experiment are used to formulate two hierarchies of representational languages for graphy theory. The first hierarchy is based on the superficial terminology and knowledge of the thought experiment. Generated by formal grammars with set-theoretic semantics, this eminently reasonable approach ultimately fails to meet the factual knowledge criteria. The second hierarchy uses declarative expressions, each of which has a semantic interpretation as a stylized, recursive algorithm that defines a class by generating it correctly and completely. A simple version of one such representation is validated by a successful, implemented system called Graph Theorist (GT) for mathematical research in graph theory. GT generates correct examples, defines and explores new graph theory properties, and conjectures and proves theorems.Several themes run through this paper. The first is the dual goals, behavioral objectives and factural knowledge to be discovered, and the multiplicity of their demands on a discovery system. The second theme is the central role of object classes to knowledge representation. The third is the increased power and flexibility of a constructive (generator) definition over the more traditional predicate (tester) definition. The final theme is the importance of examples and recursion in mathematical knowledge. The results provide important guidance for further research in the simulation of mathematical discovery.     

Designing a decompositional rule extraction algorithm for neural networks with bound decomposition tree

The neural networks are successfully applied to many applications in different domains. However, due to the results made by the neural networks are difficult to explain the decision process of neural networks is supposed as a black box. The explanation of reasoning is important to some applications such like credit approval application and medical diagnosing software. Therefore, the rule extraction algorithm is becoming more and more important in explaining the extracted rules from the neural networks. In this paper, a decompositional algorithm is analyzed and designed to extract rules from neural networks. The algorithm is simple but efficient; can reduce the extracted rules but improve the efficiency of the algorithm at the same time. Moreover, the algorithm is compared to the other two algorithms, M-of-N and Garcez, by solving the MONK’s problem.     

Implementing expert system rule conditions by neural networks

The relation of subsymbolic (neural computing) and symbolic computing has been a topic of intense discussion. We address some of the drawbacks of current expert system technology and study the possibility of using neural computing principles to improve their competence. In this paper we focus on the problem of using neural networks to implement expert system rule conditions. Our approach allows symbolic inference engines to make direct use of complex sensory input via so called detector predicates. We also discuss the use of self organizing Kohonen networks as a means to determine those attributes (properties) of data that reflect meaningful statistical relationships in the expert system input space. This mechanism can be used to address the defficult problem of conceptual clustering of information. The concepts introduced are illustrated by two application examples: an automatic inspection system for circuit packs and an expert system for respiratory and anesthesia monitoring. The adopted approach differs from the earlier research on the use of neural networks as expert systems, where the only method to obtain knowledge is learning from training data. In our approach the synergy of rules and detector predicates combines the advantages of both worlds: it maintains the clarity of the rule-based knowledge representation at the higher reasoning levels without sacrificing the power of noise-tolerant pattern association offered by neural computing methods. This research is supported by Technology Development Center (TEKES) in Software Technology Programme (FINSOFT). Part of this work was done while the author was visiting AT & T Bell Laboratories.     

基于SNMP的网络拓扑发现算法

网络拓扑发现是网络管理中一项非常重要的技术.鉴于现在越来越多的网络设备都支持SNMP协议,提出了基于SNMP的网络层拓扑发现和链路层拓扑发现算法.网络层的拓扑发现算法有效的解决了路由器的多IP地址问题.对于链路层的拓扑发现,通过结合基于网桥转发表和基于网桥生成树两种算法的优点,提出了一种新的链路层拓扑发现算法.该算法能够快速准确地计算出整个被管网络的二层和三层拓扑结构,而且适用范围广泛.     

The construction of a fuzzy inference network by extension of the rule inference network

Fuzzy logic can bring about inappropriate inferences as a result of ignoring some information in the reasoning process. Neural networks are powerful tools for pattern processing, but are not appropriate for the logical reasoning needed to model human knowledge. The use of a neural logic network derived from a modified neural network, however, makes logical reasoning possible. In this paper, we construct a fuzzy inference network by extending the rule–inference network based on an existing neural logic network. The propagation rule used in the existing rule–inference network is modified and applied. In order to determine the belief value of a proposition pertaining to the execution part of the fuzzy rules in a fuzzy inference network, the nodes connected to the proposition to be inferenced should be searched for. The search costs are compared and evaluated through application of sequential and priority searches for all the connected nodes.     

人际信息交互中低功耗网络发现应用研究

无线人际信息交互网络围绕人际信息交互的特点展开。重点关注无线通信中的网络发现中的能耗问题,利用IEEE802.15.4网络的超帧结构,提出了一种通过变换超帧参数实现的低功耗网络发现算法,分析了该算法的能耗与时间约束的关系。最后,在硬件平台上开发了电子名片应用以测试网络发现算法的有效性。     

计算机网络拓扑发现技术研究

网络拓扑发现是通过收集网络传输的信息来研究网络连通结构的一项技术,在网络管理和网络安全评估发面具有重要意义。针对网络拓扑发现的关键问题,对网络拓扑发现的研究内容和研究目标进行了概括,分析了目前网络拓扑发现技术的国内外研究现状,从协议的角度归纳了目前几种网络拓扑发现技术的研究方法,阐述了每种方法的实现机理,同时指出了每种方法的使用范围和不足,最后讨论了网络拓扑发现技术的发展趋势。     

基于OSPF服务器的网络拓扑发现

OSPF是一种链路状态路由协议,其分组中含有网络拓扑信息。运行一台OSPF服务器。像路由器一样在网络中收集OSPF分组,便能通过这些路由分组快速准确可靠地发现网络拓扑。描述了OSPF服务器的设计思路,并利用隧道技术有效解决了OSPF服务器在部署上的问题。在实验中OSPF服务器取得了良好的效果。     

宽带接入网络拓扑搜索算法研究

随着宽带接入网络技术的发展,如何有效地进行网络管理的问题日益突出。网络拓扑搜索是宽带接入网络管理软件开发的重要内容。本文在比较常用的基于SNMP,ARP和ICMP等3种拓扑搜索方法的基础上,提出了一种SNMP路由表与ICMP ping相结合的网络拓扑搜索算法－－综合搜索法,根据该算法开发实现了拓扑自动搜索程序。同时,在以太接入宽带网络环境试验应用中证明了该算法的可行性。     

Classifying expressions by cascade-correlation neural network

The classification of facial expressions by cascade-correlation neural networks [1] is described. A success rate of 100% over the training data for each of six categories of emotion —happiness, sadness, anger, surprise, fear and disgust — and of up to 87.5% over the same categories for the test data, has been achieved. By using single emotion nets for each category, together with a Net for Resolution, the results represent a 12.5% success rate beyond what was achieved by a single net classifying over all six emotion categories. Face data in the form of 10 hand measurements made on 94 well validated full face photographs [2] provided the input data after normalisation. These measures, among others, had previously been shown to discriminate between emotions [3].     

计算机网络安全评价中对于神经网络的应用探究

知识经济的到来促进了信息化的发展,计算机和网络技术也在发展和变化,影响网络安全的不确定因素也日趋变化,种类形式增加.网络安全是当前网络应用者不可轻视和低估的问题,为了解决和减少越来越突出的网络安全问题,探讨计算机网络评价对于神经网络的应用价值是具有重要意义的.神经网络的应用可大幅度的降低计算机网络安全风险,降低其带来的损失.神经网络在计算机安全评价中具有很大的实际效用和价值.     

Improving the Fuzzy Min-Max neural network with a K-nearest hyperbox expansion rule for pattern classification

An improved Fuzzy Min-Max (FMM) neural network with a K-nearest hyperbox expansion rule is proposed in this paper. The aim is to reduce the FMM network complexity for undertaking pattern classification tasks. In the proposed model, a useful modification to overcome a number of identified limitations of the original FMM network and to improve its classification performance is derived. In particular, the K-nearest hyperbox expansion rule is formulated to reduce the network complexity by avoiding the creation of too many small hyperboxes within the vicinity of the winning hyperbox during the FMM learning stage. The effectiveness of the proposed model is evaluated using a number of benchmark data sets. The results compare favorably with those from various FMM variants and other existing classifiers.     

Knowledge discovery using genetic algorithm for maritime situational awareness

Due to the large volume of data related to vessels, to manually pore through and to analyze the information in a bid to identify potential maritime threat is tedious, if at all possible. This study aims to enhance maritime situational awareness through the use of computational intelligence techniques in detecting anomalies. A knowledge discovery system based on genetic algorithm termed as GeMASS was proposed and investigated in this research. In the development of GeMASS, a machine learning approach was applied to discover knowledge that is applicable in characterizing maritime security threats. Such knowledge is often implicit in datasets and difficult to discover by human analysts. As the knowledge relevant to maritime security may vary from time to time, GeMASS was specified to learn from streaming data and to generate up-to-date knowledge in a dynamic fashion. Based on the knowledge discovered, the system functions to screen vessels for anomalies in real-time. Traditionally in maritime security studies, datasets that are applied as knowledge sources are related to vessels’ geographical and movement information. This study investigated a novel leverage of multiple data sources, including Automatic Identification System, classification societies, and port management and security systems for the enhancement of maritime security. A prototype of GeMASS was developed and employed as a vehicle to study and demonstrate the functions of the proposed methodology.     

Global optimisation in neural network training

This paper presents a new neural network training scheme for pattern recognition applications. Our training technique is a hybrid scheme which involves, firstly, the use of the efficient BFGS optimisation method for locating minima of the total error function and, secondly, the use of genetic algorithms for finding a global minimum. This paper also describes experiments that compare the performance of our scheme with three other hybrid schemes of this kind when applied to challenging pattern recognition problems. Experiments have shown that our scheme gives better results than others.     

Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition

The aim of this paper is to estimate the fault location on transmission lines quickly and accurately. The faulty current and voltage signals obtained from a simulation are decomposed by wavelet packet transform (WPT). The extracted features are applied to artificial neural network (ANN) for estimating fault location. As data sets increase in size, their analysis become more complicated and time consuming. The energy and entropy criterion are applied to wavelet packet coefficients to decrease the size of feature vectors. The test results of ANN demonstrate that the applying of energy criterion to current signals after WPT is a very powerful and reliable method for reducing data sets in size and hence estimating fault locations on transmission lines quickly and accurately.     

基于神经网络的印刷体数字字符的识别

提出了一种基于BP网络的印刷体数字字符的识别方法。通过对BP网络的研究与学习,设计了一种结构合理,收敛速率快的BP网络。实验结果表明,该方法对标准的印刷体数字字符的识别率达到了100％,对有1～3度倾斜角度的字符识别率也达到了96％以上。     

Artificial wavelet neuro-fuzzy model based on parallel wavelet network and neural network

From the well-known advantages and valuable features of wavelets when used in neural network, two type of networks (i.e., SWNN and MWNN) have been proposed. These networks are single hidden layer network. Each neuron in the hidden layer is comprised of wavelet and sigmoidal activation functions. First model is derived from adding the outputs of wavelet and sigmoidal activation functions, while in the second model outputs of wavelet and sigmoidal activation function are multiplied together. Using these proposed networks in consequent part of the neuro-fuzzy model, which result summation wavelet neuro-fuzzy and multiplication wavelet neuro-fuzzy models, are also proposed. Different types of wavelet function are tested with proposed networks and fuzzy models on four different types of examples. Convergence of the learning process is also guaranteed by performing stability analysis using Lyapunov function.     

对基于知识发现的神经元网络集成方法的研究

该文对应用知识发现技术训练神经元网络集成的方法进行了研究,提出了以并行操作的方式结合归纳学习所获取的知识和演绎学习所获取的知识的神经元网络集成模型KBNNE(Knowledge-basedNeuralNetworkEnsem-bles)。实验表明,通过调节所获取知识的权重因子,新模型可以有效提高网络集成的性能。