基于神经网络的雷达辐射源体制和用途智能识别

本文描述一种采用BP神经网络对雷达辐射源体制和用途进行智能识别的方法 ,包括理论分析、神经网络结构设计、推理和学习算法设计、编程算法设计、实验结果比较与分析等。实验表明 ,该方法有着良好的识别效果     

动态模糊随机信息处理的数学方法

本文系统地概述了我们近年来提出的动态模糊随机信息处理的数学方法,内容包括模糊随机变量、模糊随机过程和模糊随机微分方程的基本解法等方面的基本概念、基本定义和某些重要的定理,以及动态系统的模糊随机响应与可靠性分析的方法等。这些方法是为我们研究工程实际问题的需要逐步发展起来的,对于处理某些类型的问题简便实用。     

Fuzzy集的基数

关于Fuzzy集的基数，[1]中曾对有限支集的Fuzzy集以及极苛刻条件下的无限Fuzzy集给出过一种定义。但是，正如本文将要指出的那样，该定义是不合理的。本文将从研究Fuzzy映射入手，给出Fuzzy集基数的一般性定义。基于这一定义，不但得到有关基数的大部分结论，而且有其自身的特殊性。     

基于模糊度测度和改进遗传算法的图像分割

为了利用模糊集理论进行图像分割,本文提出了一种模糊度测度函数,它衡量了背景模糊集和某一个二值图像的相似性:模糊度越小,这种相似程度越大;并且对模糊度的计算只需要进行简单的比较运算和加法运算,便于硬件实现。本文利用遗传算法求取最佳阈值,针对遗传算法的某些不足之处.我们利用精英策略和模拟退火的思想,对其做了一些修正。实验结果表明:相对于Shannon熵方法和模糊熵方法,本文方法取得了较好的分割结果,并且具有较强的抗噪声能力。     

一类可由基本模型嵌套组成的工作流系统的性能分析方法

工作流是近年来网络领域里研究的一个热点,对工作流模型的性能分析是工作流研究的一个重要内容.实际的模型往往规模大而复杂,这为分析其性能带来了很大的困难.但有一类模型可由工作流的四种基本模型(顺序、并行、选择、循环)嵌套组成,对于这类模型,本文给出了计算其性能乘积解的数学公式.文中给出了如何由这四种基本模型嵌套组成实际模型的方法,推导出了计算这四种基本模型的性能等价公式,并按建立实际模型相反的顺序,用由里到外逐层的分析方法,给出了计算实际模型的性能乘积解.     

基于概念网络的文本信息监控技术

由于网上信息的迅速增长,文本信息监控已经成为网上信息管理中的热点研究课题。本文在模糊集和语义网络的理论基础上,构建了模糊值动态约束性概念网络,介绍了该概念网络的基本构建方法,并提出了基于概念网络的文本分析算法。     

Fuzzy Entropy： Axiomatic Definition and Neural Networks Model

The measure of uncertainty is adopted as a measure of information. The measures of fuzziness are known as fuzzy information measures. The measure of a quantity of fuzzy information gained from a fuzzy set or fuzzy system is known as fuzzy entropy. Fuzzy entropy has been focused and studied by many researchers in various fields. In this paper, firstly, the axiomatic definition of fuzzy entropy is discussed. Then, neural networks model of fuzzy entropy is proposed, based on the computing capability of neural networks. In the end, two examples are discussed to show the efficiency of the model.     

自适应比例因子模糊控制器在异步电动机调速系统中的应用

为了降低转子电阻变化对异步电动机矢量控制系统性能的影响，本文把自适应比例因子模糊控制器应用到了矢量控制系统中。介绍了自适应比例因子模糊控制方法的綦本原理，给出了使用自适应比例因子模糊控制器的异步电动机矢量控制系统的结构框图，对主模糊控制器和辅助模糊控制器进行了设计。仿真实验结果表明，采用这种控制方法的调速系统不仅具有优良的动态性能，而且对转子电阻变化具有很强的鲁棒性。     

广电城域网中IP地址的规划

介绍广电城域网IP地址规划、分配的原则和方法以及利用IP地址计算器工具软件协助实现IP地址的计算等。     

An Approach to Fuzzy Noncooperative Nash Games

Systems that involve more than one decision maker are often optimized using the theory of games. In the traditional game theory, it is assumed that each player has a well-defined quantitative utility function over a set of the player decision space. Each player attempts to maximize/minimize his/her own expected utility and each is assumed to know the extensive game in full. At present, it cannot be claimed that the first assumption has been shown to be true in a wide variety of situations involving complex problems in economics, engineering, social and political sciences due to the difficulty inherent in defining an adequate utility function for each player in these types of problems. On the other hand, in many of such complex problems, each player has a heuristic knowledge of the desires of the other players and a heuristic knowledge of the control choices that they will make in order to meet their ends.In this paper, we utilize fuzzy set theory in order to incorporate the players' heuristic knowledge of decision making into the framework of conventional game theory or ordinal game theory. We define a new approach to N-person static fuzzy noncooperative games and develop a solution concept such as Nash for these types of games. We show that this general formulation of fuzzy noncooperative games can be applied to solve multidecision-making problems where no objective function is specified. The computational procedure is illustrated via application to a multiagent optimization problem dealing with the design and operation of future military operations.