基于专家系统和神经网络的网络入侵检测系统

研究网络安全问题,网络入侵方式具有多样性和不确定性,当前大多数入侵检测系统检测正确率低,误报和漏报率高的缺陷。为了提高网络入侵检测正确率,保证网络安全,提出一种基于专家系统和神经网络的智能入侵检测系统。首先采用专家系统对已知网络入侵进行检测,然后采用神经网络对专家系统不能发现的未知网络入侵进行检测,最后利用神经网络检测结果对专家系统规则库进行更新。采用网络入侵检测数据库KDD CUP 99进行仿真,结果表明,智能入侵检测系统提高了网络入侵检测的正确率,有效降低了网络入侵的误报率和漏报率,为网络入侵提供了一种新的有效的检测工具。     

基于神经网络和专家系统的故障诊断

分析了专家系统和神经网络在故障诊断中的应用，针对两种技术各自的优缺点，建立了一个基于神经网络和专家系统的采煤机集成故障诊断系统，介绍了系统的结构，诊断策略及神经网络的学习算法，并给出了仿真结果。     

一个基于神经网络的专家系统

医疗专家系统主要使用基于知识的技术，其中的决策规则和策略来自于人类的专家。把这些知识和各种推理方法结合，可以建立一个模拟专家决策过程的系统。建立这样一个系统，需要经常与专家磋商，以获取专家的知识，因而需要大量的时间和精力。为此，本文提出直接从数据中提取有效的信息，即用神经网络提取隐含在大量数据中对医疗诊断有效的信息，继之与基于规则的知识，各种推理方法相结合，建立一个神经网络专家系统。     

基于神经网络的专家系统体系研究

本文分析传统专家系统存在的主要问题，阐述了神经网络专家系统的基本原理，并给出具体模块框架结构。     

基于粗神经网络的瓦斯预测专家系统

瓦斯爆炸是我国煤矿生产中最常见的灾害事故,目前治理瓦斯爆炸的主要手段是预测瓦斯突出。以专家系统为核心,利用神经网络强的自学习能力来建立专家系统的知识库,并使用粗糙集对神经网络的构造和学习所需的样本数据进行预处理,构建了基于粗神经网络的瓦斯预测专家系统,详细介绍了系统的总体结构和工作过程,并以采集到的实时数据进行仿真实验,结果表明该系统有较好的预测能力。提高了瓦斯预测的准确性和及时性。     

基于PCA和神经网络的故障诊断技术

提出一种基于PCA和神经网络的故障诊断/识别方法,利用主元分析法提取故障样本集的主元,实现故障样本的最优压缩,简化故障诊断中神经网络分类器的结构,提高神经网络的分类速度和测试精度。仿真结果表明,该方法可以有效减少输入层神经元个数,提高神经网络模型的学习效率和诊断的准确性,具有良好的故障识别能力。     

基于神经网络的专家系统体系结构

本文分析了传统专家系统存在的主要问题,阐述了神经网络专家系统的基本原理,并给出其模块结构。     

基于神经网络的专家系统体系结构

本文分析了传统专家系统存在的主要问题,阐述了神经网络专家系统的基本原理,并给出其模块结构.     

基于神经网络的植物分类专家系统

本文介绍了植物分类专家系统（ＰＴＥＳ）的设计与实现。该系统采用神经网络知识表示方法表示知识，是一个基于神经网络（Ｎｅｕｒａｌ Ｎ ｅｔｗｏｒｋｓ－ＮＮ）的植物分类专家系统。     

基于人工神经网络的葡萄病害诊断专家系统

设计了一种基于人工神经网络的葡萄病害诊断专家系统。以常见的18种主要的葡萄病害为研究对象,将专家知识转换为诊断规则,并作为学习样本输入神经网络进行训练,形成人工神经网络推理机。同时,采用知识库、规则推理和人工神经网络推理相结合的系统结构来优化专家系统,在提高专家系统自学能力的同时也提高了系统的响应速度。采用C#、Matlab和.NET技术混合编程实现专家系统,实验结果表明该系统有较高的诊断准确率并能稳定运行。该系统在Web上运行,更有利于系统的推广应用。     

一种改进的神经网络机械故障诊断专家系统

针对传统BP神经网络训练中收敛速度较慢的缺点,提出一种基于L-M算法的神经网络应用于机械设备故障诊断的专家系统。论述了神经网络的专家系统结构,并以7216圆锥轴承试验研究为例,建立了基于该算法的故障诊断模型。仿真结果表明:该模型显著缩短了训练时间,具有较高的准确性。运用该神经网络专家系统进行机械故障诊断是有效的。     

基于BP的燃气管道评估专家系统设计与实现

BP神经网络具有并行处理、自适应学习、容错能力好等优点,根据地下燃气管道安全评估的应用要求,对其算法进行改进,结合专家系统的设计思路,构造了一个基于BP神经网络的地下燃气管道安全评估专家系统,包括知识表示、知识荻取和推理解释等模块.然后利用已有的样本编制软件进行实验,结果表明,该系统评估正确率达91%,推理效率高、扩展性好,从而很好地满足了实际应用的要求.神经网络为专家系统的发展提供了一条新的途径.     

Research and Design of a Fuzzy Neural Expert System

We have developed a fuzzy neural expert system that has the precision and learning ability of a neural network.Knowledge is acquired from domain experts as fuzzy rules and membership functions.Then,they are converted into a neural network which implements fuzzy inference without rule matching.The neural network is applied to problem-solving and learns from the data obtained during operation to enhance the accuracy.The learning ability of the neural network makes it easy to modify the membership functions defined by domain experts.Also,by modifying the weights of neural networks adaptively,the problem of belief propagation in conventional expert systems can be solved easily.Converting the neural network back into fuzzy rules and membership functions helps explain the inner representation and operation of the neural network.     

Integrating artificial neural networks with rule-based expert systems

The Rule-Based (RB) and the Artificial Neural Network (ANN) approaches to expert systems development have each demonstrated some specific advantages and disadvantages. These two approaches can be integrated to exploit the advantages and minimize the disadvantages of each method used alone. An RB/ANN integrated approach is proposed to facilitate the development of an expert system which provides a “high-performance” knowledge-based network, an explanation facility, and an input/output facility. In this case study an expert system designed to assist managers in forecasting the performance of stock prices is developed to demonstrate the advantages of this integrated approach and how it can enhance support for managerial decision making.     

Modeling users through an expert system and a neural network

With the number of Internet and Web users increasing rapidly, electronic service providers are competing to satisfy and better serve customers looking for information or channels of advertisement. A wide variety of browses, specialized sites, custom made software, etc. are being offered on a regular basis. However, the user has to filter through a large number of files before finding what he/she is really looking for. This paper presents a user modeling expert system, SIGMA, based on neural networks for encapsulating Internet and Web users' habits and preferences. SIGMA is an artificial intelligence application designed to answer an Internet client needs and preferences. It analyses the user supplied demographic data and the monitored transactions then generate a tailored profile that is ultimately used to filter what information is being passed on to him/her in an effort to reduce and hopefully eliminate the time and energy expended in sifting through raw and often unwanted data.     

面向对象的无损检测工艺设计专家系统

无损检测工艺确定在压力容器制造中占有重要地位,基于面向对象技术建立X射线、超声波、磁粉、渗透等检测技术的综合无损检测工艺专家系统,系统由主控模块、规范设计模块、监测管理模块以及知识库等组成。开发结果表明,采用面向对象技术不仅保证了系统运行的有效性,同时也显著提高了系统开发效率和系统可维护性和扩充性。     

基于神经网络集成的专家系统模型

提出一种基于神经网络集成的专家系统模型,并给出神经网络集成的构造算法.在该模型中神经网络集成作为专家系统的一个内嵌模块,用于专家系统的知识获取,克服了传统专家系统在知识获取中的＂瓶颈＂问题.并将该模型用于图书剔旧系统中,初步建成基于神经网络集成的图书剔旧专家系统原型.     

Filtering method of rock points based on BP neural network and principal component analysis

Filtering is an essential step in the process of obtaining rock data. To the best of our knowledge, there are no special algorithms for use in the point clouds of rock masses. Existing filtering methods remove noisy points by fitting the surface of the ground and deleting the points above the surface around a range of values. This type of methods has certain limitations in rock engineering owing the uniqueness of the particular rockmass being studied. In this paper, a method for filtering the rock points is proposed based on a backpropagation (BP) neural network and principal component analysis (PCA). In the proposed method, a PCA is applied for feature extraction, and for obtaining the dimensional information, which can be used to effectively distinguish the rock and other points at different scales. A BP neural network, which has a strong nonlinear processing capability, is then used to obtain the exact points of rock with the above characteristics. In the present paper, the efficiency of the proposed technique is illustrated by classifying steep rocky slopes as rock and vegetation. A comparison with existing methods indicates the superiority of the proposed method in terms of the point cloud filtering of rock masses.     

基于PCA和神经网络的识别方法研究

在计算机集成制造系统环境下,质量控制图是统计过程控制的重要工具,实际应用中最困难的是识别出控制图中由于异常因素造成的不同异常模式。针对这一问题展开研究,用主成分分析法作为前处理过程进行样本集的选择与优化,提出了基于PCA_改进BP算法的控制图模式智能识别方法。