基于概念主干H的知识联通及其联通测度

1 引言领域专业知识在决策系统、智能教学系统(ITS)、知识查询系统等高技术产品中具有重要的作用。知识库是机器智能的重要来源,而大量实践证明计算机真正具有智能不仅需要大型知识库,而且更需要知识间广泛的互连。     

Entropy maximisation and open queueing networks with priorities and blocking

A review is carried out on the characterisation and algorithmic implementation of an extended product-form approximation, based on the principle of maximum entropy (ME), for a wide class of arbitrary finite capacity open queueing network models (QNMs) with service and space priorities. A single server finite capacity GE/GE/1/N queue with R (R>1) distinct priority classes, compound Poisson arrival processes (CPPs) with geometrically distributed batches and generalised exponential (GE) service times is analysed via entropy maximisation, subject to suitable GE-type queueing theoretic constraints, under preemptive resume (PR) and head-of-line (HOL) scheduling rules combined with complete buffer sharing (CBS) and partial buffer sharing (PBS) management schemes stipulating a sequence of buffer thresholds {N=(N₁,…,N_R),0<N_i≤N_i−1,i=2,…,R}. The GE/GE/1/N queue is utilised, in conjunction with GE-type first two moment flow approximation formulae, as a cost-effective building block towards the establishment of a generic ME queue-by-queue decomposition algorithm for arbitrary open QNMs with space and service priorities under repetitive service blocking with random destination (RS-RD). Typical numerical results are included to illustrate the credibility of the ME algorithm against simulation for various network topologies and define experimentally pessimistic GE-type performance bounds. Remarks on the extensions of the ME algorithm to other types of blocking mechanisms, such as repetitive service blocking with fixed destination (RS-FD) and blocking-after-service (BAS), are included.     

Various hybrid methods based on genetic algorithm with fuzzy logic controller

In this paper we propose several efficient hybrid methods based on genetic algorithms and fuzzy logic. The proposed hybridization methods combine a rough search technique, a fuzzy logic controller, and a local search technique. The rough search technique is used to initialize the population of the genetic algorithm (GA), its strategy is to make large jumps in the search space in order to avoid being trapped in local optima. The fuzzy logic controller is applied to dynamically regulate the fine-tuning structure of the genetic algorithm parameters (crossover ratio and mutation ratio). The local search technique is applied to find a better solution in the convergence region after the GA loop or within the GA loop. Five algorithms including one plain GA and four hybrid GAs along with some conventional heuristics are applied to three complex optimization problems. The results are analyzed and the best hybrid algorithm is recommended.     

Dual-Priority versus Background Scheduling: A Path-Wise Comparison

In this paper, two well-known scheduling policies for real-time systems, namely background scheduling (BS) and dual-priority (DP) are compared in terms of response times for soft real-time traffic (SRT). It is proved in the preemptive as well as in the non-preemptive case that, when the SRT traffic is FIFO, the DP policy always outperforms BS for all instances of SRT tasks. When the SRT traffic is not FIFO but if all tasks are of equal size then, in the non-preemptive case, the average response times is shown to be always better under DP than under BS. As a complementary result, some non-FIFO examples where BS behaves better than DP for some SRT tasks but also on the average of the SRT response times, are given. The proofs are based on a trajectorial method that may be used for comparing other scheduling policies.     

Regularization Techniques for Numerical Approximation of PDEs with Singularities

The rate of convergence for numerical methods approximating differential equations are often drastically reduced from lack of regularity in the solution. Typical examples are problems with singular source terms or discontinuous material coefficients. We shall discuss the technique of local regularization for handling these problems. New numerical methods are presented and analyzed and numerical examples are given. Some serious deficiencies in existing regularization methods are also pointed out.     

现代谱估计在飞行器仿真模型验证中的应用

仿真模型的正确与否决定着仿真的精度和置信度，而仿真模型验证是保证仿真模型正确的有效手段。现代谱估计方法对窗口以外的行为进行合理假设，克服了古典谱估计方法的缺点，相对于古典谱估计方法有优势。重点介绍了最大熵谱估计方法的基本原理以及在飞行器仿真模型验证中的应用，并举例予以说明。     

一种用于非线性函数逼近的小波神经网络

提出一种用于非线性函数逼近的小波神经网络，给出了网络的参数训练方法。从信息熵的概念出发，改进了网络参数训练的目标函数，并利用引入动量项的最速下降法训练网络权值、尺度因子和平移因子。仿真实验表明，该小波神经网络用于非线性函数逼近时优于同等规模的BP网络，且其训练方法亦具有收敛速度快、逼近精度高等优点。     

统计机器翻译综述

本文综述了基于信源信道思想和基于最大熵思想的统计机器翻译方法并介绍了统计机器翻译的评测方法。基于信源信道的方法将翻译概率表示为一个语言模型和一个翻译模型。而基于最大熵的方法则是利用一系列实数值特征函数的线性组合来求解最优的译文。基于最大熵的统计机器翻译方法比基于信源信道的方法更具有一般性,后者可以看做前者的一个特例。     

甲骨文象形码编码方法研究

甲骨文因字形独特、年代久远,所以一直没能进行有效编码。本文吸取现代编码思想,采用模糊数学模型分析甲骨文的部件(字根)特点,对其进行模糊聚类,并使用32个字符(25个英文字母和7个阿拉伯数字)作为码元,与甲骨文中的500多个字根相对应,实现了一字一码的编码方案。在此基础上,运用信息论中的熵理论,分析了这种编码的效率和科学性,得出甲骨文编码的最佳码长大致接近于3,从而为5000多个甲骨文字进行科学编码提供理论基础。     

一种估计前馈神经网络中隐层神经元数目的新方法

前馈神经网络中隐层神经元的数目一般凭经验的给出，这种方法往往造成隐单元数目的不足或过甚，从而导致网络存储容量不够或出现学习过拟现象，本研究提出了一种基于信息熵的估计三层前馈神经网络隐结点数目的方法，该方法首先利用训练集来训练具有足够隐单元数目的初始神经网络，然后计算训练集中能被训练过的神经网络正确识别的样本在隐层神经元的激活值，并对其进行排序，计算这些激活值的各种划分的信息增益，从而构造能将整个样本空间正确划分的决策树，最后遍历整棵树寻找重要的相关隐层神经元，并删除冗余无关的其它隐单元，从而估计神经网络中隐层神经元的较佳数目，文章最后以构造用于茶叶品质评定的具有较佳隐单元数目的神经网络为例，介绍本方法的使用，结果表明，本方法能有效估计前馈神经网络的隐单元数目。