改进分块支持向量聚类在入侵检测中的应用

支持向量聚类(SVC)是在支持向量机的思想上发展而来一种聚类方法,针对其处理大规模数据集速度缓慢的缺点,提出了一种改进的分块支持向量聚类算法。改进的算法分为三个阶段:前期的预处理,中期的分块算法,后期的改进标类算法。提出的方法显著加快了SVC的速度,在保持原来SVC算法的优点的基础上,对大规模数据集以及非均匀分布数据集等具有良好的效果。将其应用到网络入侵检测,实验结果表明改进的算法行之有效。     

带有区间约束的产品设计参数模糊推理法及其在继电器智能设计中的应用

在产品设计规则和计算式中,常存在一些只能在某一范围内取值的待定系数(设为a,且a∈[aL,aH]).a的取值通常受到多种因素的影响和约束,而这种约束往往带有模糊性.为此,首先根据设计规则中各种影响因素对a的模糊约束关系,分别构造两者的备择集以及备择集元素在其论域上的模糊集合,然后基于模糊综合决策方法来确定a.本方法剔除了以往在解决这一问题时,由人来决定a取值的主观性,适用于产品的智能化设计.     

基于模糊神经网络的感应加热电源温度控制

通过对原有感应加热电源温度控制方法的分析，提出了一种适合于非线性系统基于模糊神经网络的温度控制方法，并介绍了该系统的组成及实现方法。     

模糊综合评判法在装备采购招标中的应用

根据我军武器装备采购招标的必要性和可行性，以及传统装备采购招标方法的局限性．详细阐述了在装备采购招标中运用模糊综合评判法进行评标的基本原理和主要步骤，并通过一个实例验证了模糊综合评判法在装备采购招标中的可行性和科学性。     

基于PLC在连铸车间浊环污水处理中的应用

针对攀钢转炉炼钢连铸车间循环冷却水处理过程中出现的:系统复杂、人员配备多,难以建立准确数学模型的特点,本文提出了一种用抗干扰能力强的PLC对系统进行模糊控制的改造方案。该方案实现简单,节省岗位,降低成本,能取得良好的调节品质。     

Fuzzy logic for signal prediction in nuclear systems

In this paper, we present a fuzzy logic-based algorithm which allows to build a predictive model of an evolving signal. The if-then rules are inferred from the available input-output data. The errors of the model predictions with respect to the measured plant signals can then be used to detect, in real-time, any deviation of the process due either to a component's failure or to an operator's action. Applications of the approach are presented with respect to a chaotic time series of literature and to the water level in the steam generator of a PWR.     

计算机数控串珠绳锯切割机

采用模糊控制技术、变频传动技术、步进驱动技术研制出计算机数控串珠绳锯切割机.该机使用带闭环的变频传动技术取代传统的伺服传动,切割精度为0.15mm.采用IPC工控机和西门子S7-200PLC为主控机,人机界面好,易于编程.改变生产品种,只需修改数控程序,而不需制做模型,大大节省了投资.     

基于蚁群聚类算法的离群挖掘方法

离群挖掘是数据挖掘研究的重要内容,在实际生活中获得广泛应用。该文首先给出了离群数据的量化定义,并用基于蚁群的聚类学习方法,产生了状态空间的整体特征。然后结合具体的设备对象,提出了离群数据的挖掘方法。最后进行了实验验证,结果表明该文提出的方法是有效的。     

Colour classification by neural networks in graphic arts

This paper presents a hierarchical modular neural network for colour classification in graphic arts, capable of distinguishing among very similar colour classes. The network performs analysis in a rough to fine fashion, and is able to achieve a high average classification speed and a low classification error. In the rough stage of the analysis, clusters of highly overlapping colour classes are detected. Discrimination between such colour classes is performed in the next stage by using additional colour information from the surroundings of the pixel being classified. Committees of networks make decisions in the next stage. Outputs of members of the committees are adaptively fused through the BADD defuzzification strategy or the discrete Choquet fuzzy integral. The structure of the network is automatically established during the training process. Experimental investigations show the capability of the network to distinguish among very similar colour classes that can occur in multicoloured printed pictures. The classification accuracy obtained is sufficient for the network to be used for inspecting the quality of multicoloured prints.     

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.