基于信息系统的确定有限自动机最小化算法

目前,确定有限自动机(DFA)最小化问题多侧重于理论研究,尚无太多便于实现的算法,为此,对确定有限自动机最小化方法进行了研究,提出将DFA转换为信息系统,基于等价类划分方法简化信息系统,再将简化的信息系统转换为最小化DFA;针对上述处理过程,给出一个基于分治思想的DFA最小化算法,在平均情况下该算法的时间复杂度为O(n log n),空间复杂度为O(n)。最后通过实例验证了所提算法的正确性。     

确定型模糊多重集有限自动机的极小化

主要研究确定型模糊多重集有限自动机的状态极小化问题。给出了模糊多重集有限自动机的同余和同态概念,并利用同余和同态关系研究了确定型模糊多重集有限自动机的极小化问题。进一步从确定型模糊多重集有限自动机自身出发,构造出极小模糊多重集有限自动机,并给出了极小化的算法。     

有限状态自动机的并行确定化及过程分析

本文通过对并行环境下非确定有限自动机和确定有限自动机的等价性和转换进行研究，详细分析了非确定有限自动机到确定有限自动机的并行转换方法及算法，并以实例给出了其间并行转化的过程。     

基于等价关系的有穷自动机最小化方法

主要介绍了有穷自动机的基础知识,研究了有穷自动机的等价性,并在确定型有穷自动机的状态集上引入等价关系,给出了自动机的最小化过程。利用等价归并算法,可以将某一给定的确定型有穷自动机状态集上的等价状态归并掉．生成与其等价的最小化的确定型有穷自动机。     

基于等价关系的有穷自动机最小化方法

主要介绍了有穷自动机的基础知识,研究了有穷自动机的等价性,并在确定型有穷自动机的状态集上引入等价关系,给出了自动机的最小化过程。利用等价归并算法,可以将某一给定的确定型有穷自动机状态集上的等价状态归并掉,生成与其等价的最小化的确定型有穷自动机。     

不确定广义大系统有限时间鲁棒分散控制

研究不确定连续广义大系统的有限时间鲁棒分散控制问题,设计系统的有限时间鲁棒分散状态反馈控制器.首先应用广义Lyapunov 函数法,给出不确定广义大系统有限时间鲁棒稳定的充分条件;其次,给出不确定广义大系统应用分散状态反馈控制器鲁棒镇定的充分条件和有限时间鲁棒分散控制器的设计方法;最后,通过仿真例子验证所提出方法的有效性.     

有限自动机在复合事件检测中的应用

针对早期系统只提供原子事件的检测机制,不能检测由原子事件组成的复合事件的问题,提出了用有限自动机来检测复合事件的方法.说明了复合事件的组成和表达式,利用自动机原理对复合事件的检测模式进行了分析,给出了复合事件检测的具体过程:从事件表达式到不确定的有限自动机,从不确定的有限自动机到最小化确定的有限自动机,再用程序实现了确定的有限自动机.实例表明,自动机模型是检测复合事件的一种有效实现方式.     

基于逻辑程序的免疫识别器动态覆盖性初探

免疫识别器的动态覆盖与人工免疫系统的有效性具有重要的关联关系,应该用尽可能少的识别器覆盖尽可能多的NONSELF空间。根据识别器的动态覆盖性原理,引入逻辑程序来表示免疫识别器,并运用逻辑程序的更新特性,试图消除识别器所识别出的NONSELF空间的冗余,降低识别器的浓度,增强识别器的动态覆盖性。     

免疫识别器动态覆盖性的选择策略

免疫识别器的浓度、系统资源占用与人工免疫系统的有效性具有重要的关联关系。在尽可能少地占用系统资源,即降低识别器浓度的前提下,为了更有效地识别NONSELF,保证识别器的动态覆盖性是关键。文章根据机体免疫学的动力学原理,提出了一种动态选择算法,通过对识别器的优胜劣汰提高识别器集合的整体识别能力。     

VU-CN1316型纸币识别器通信协议及实现①

通过对VU-CN1316型纸币识别器通信协议中的字节传送格式、数据块传输和典型会话过程的剖析,并以VB6.0为开发平台提出了一种纸币识别器（Bill Validator）与工业控制计算机（IPC）之间的通信方法,结合实际开发的小区电费自动缴费系统详细的叙述了该方法的实现过程。     

模糊识别器与有穷自动机的等价性

针对模糊识别器与有穷自动机的关系,证明了当输入字母表相同时,任给一个模糊识别器,必然存在一个有穷自动机,使得模糊识别器的行为与有穷自动机所接受的语言相同;反之,任给一个有穷自动机,必然存在一个模糊识别器,使得有穷自动机所接受的语言与模糊识别器的行为相同,从而得出它们之间的等价性。     

Intuitionistic fuzzy recognizers and intuitionistic fuzzy finite automata

Intuitionistic fuzzy recognizers and intuitionistic fuzzy finite automata are discussed. The notions of intuitionistic fuzzy recognizer, complete accessible intuitionistic fuzzy recognizer, intuitionistic fuzzy finite automata, deterministic intuitionistic fuzzy finite automata, and intuitionistic fuzzy language are introduced. It is shown that the languages recognized by intuitionistic fuzzy recognizer are regular, and the intuitionistic fuzzy languages recognized by the intuitionistic fuzzy finite automaton and the intuitionistic fuzzy languages recognized by deterministic intuitionistic fuzzy finite automaton are equivalent. This work is supported by National Science Foundation of China (Grant No.10571112), “TRAPOYT” of China and National 973 Foundation Research Program(Grant No.2002CB312200).     

A hybrid named entity recognizer for Turkish

Named entity recognition is an important subfield of the broader research area of information extraction from textual data. Yet, named entity recognition research conducted on Turkish texts is still rare as compared to related research carried out on other languages such as English, Spanish, Chinese, and Japanese. In this study, we present a hybrid named entity recognizer for Turkish, which is based on a manually engineered rule based recognizer that we have proposed. Since rule based systems for specific domains require their knowledge sources to be manually revised when ported to other domains, we enrich our rule based recognizer and turn it into a hybrid recognizer so that it learns from annotated data when available and improves its knowledge sources accordingly. The hybrid recognizer is originally engineered for generic news texts, but with its learning capability, it is improved to be applicable to that of financial news texts, historical texts, and child stories as well, without human intervention. Both the hybrid recognizer and its rule based predecessor are evaluated on the same corpora and the hybrid recognizer achieves better results as compared to its predecessor. The proposed hybrid named entity recognizer is significant since it is the first hybrid recognizer proposal for Turkish addressing the above porting problem considering that Turkish possesses different structural properties compared to widely studied languages such as English and there is very limited information extraction research conducted on Turkish texts. Moreover, the employment of the proposed hybrid recognizer for semantic video indexing is shown as a case study on Turkish news videos. The genuine textual and video corpora utilized throughout the paper are compiled and annotated by the authors due to the lack of publicly available annotated corpora for information extraction research on Turkish texts.     

Building a compact online MRF recognizer for large character set by structured dictionary representation and vector quantization technique

This paper describes a method for building a compact online Markov random field (MRF) recognizer for large handwritten Japanese character set using structured dictionary representation and vector quantization (VQ) technique. The method splits character patterns into radicals, whose models by MRF are shared by different character classes such that a character model is constructed from the constituent radical models. Many distinct radicals are shared by many character classes with the result that the storage space of model dictionary can be saved. Moreover, in order to further compress the parameters, VQ technique to cluster parameter sequences of the mean vectors and covariance matrixes for MRF unary features and binary features as well as the transition probabilities of each state into groups was employed. By sharing a common parameter sequence for each group, the dictionary of the MRF recognizer can be greatly compressed without recognition accuracy loss.     

(r,t)阶存贮拟线性有限自动机的极小化

实现将一个有限自动机极小化是一个困难的问题。通过零状态考虑了一类(r,t)阶存贮拟线性有限自动机极小化的判断方法,给出了极小线性有限自动机的描述,最后给出了基于系数矩阵的极小化算法实现。     

多层DGMM识别器在中国手语识别中的应用

手语是聋人使用的语言,是由手形动作辅之以表情姿势由符号构成的比较稳定的表达系统 ,是一种靠动作/视觉交际的语言.手语识别的研究目标是让机器“看懂”聋人的语言.手 语识别和手语合成相结合,构成一个“人-机手语翻译系统”,便于聋人与周围环境的交 流.手语识别问题是动态手势信号即手语信号的识别问题.考虑到系统的实时性及识别效率, 该系统选取Cyberglove型号数据手套作为手语输入设备,采用DGMM(dynamic Gaussian mixt ure model)作为系统的识别技术,并根据中国手语的具体特点,在识别模块中选取了多层识 别器,可识别中国手语字典中的274个词条,识别率为97.4%.与基于单个DGMM的识别系统比 较,这种模型的识别精度与单个DGMM模型的识别精度基本相同,但其识别速度比单个DGMM的 识别速度有明显的提高.     

Feature-based recognition of control chart patterns

Control charts primarily in the form of chart are widely used to identify the situations when control actions will be needed for manufacturing systems. Various types of patterns are observed in control charts. Identification of these control chart patterns (CCPs) can provide clues to potential quality problems in the manufacturing process. Each type of control chart pattern has its own geometric shape and various related features can represent this shape. Feature-based approaches can facilitate efficient pattern recognition since extracted shape features represent the main characteristics of the patterns in a condensed form. In this paper, a set of eight new features, extraction of which does not call for utilizing the experience and skill of the user in any form, is presented. Two feature-based approaches using heuristics and artificial neural network (ANN) are developed, which are capable of recognizing eight most commonly observed CCPs including stratification and systematic patterns. Relative performances of the feature-based heuristic and feature-based ANN recognizers are extensively studied using synthetic pattern data. The feature-based ANN recognizer results in better recognition performance and generalization compared to the feature-based heuristic recognizer.     

离散事件系统间歇性故障的安全诊断

离散事件系统的间歇性故障诊断能够将系统中发生的间歇性故障及时诊断出来,但在诊断期间的系统可能会执行不安全操作.针对间歇性故障在诊断期间的安全性问题,提出一种基于事件的安全诊断方法.首先对发生间歇性故障的离散事件系统进行建模,并给出系统间歇性故障的安全可诊断性的形式化定义.然后通过构造非法语言识别器对系统的非法操作进行识别,并在此基础上构建一个安全验证器,由此得到一个关于系统间歇性故障安全可诊断性的充分必要条件,实现离散事件系统对间歇性故障的安全诊断.这种安全诊断既保证了间歇性故障一旦发生即能被及时诊断出来,又确保了在故障诊断期间系统不会执行任何不安全操作.     

Mizumoto格值有限自动机及其最小化

提出取值为格半群的Mizumoto格值有限自动机的概念,得到基于模糊字符串的Mizumoto格值有限自动机的扩张模型,并详细讨论了其性质。同时建立了扩张Mizumoto格值有限自动机与标准扩张Mizumoto格值有限自动机的等价性,在此基础上给出了其最小化算法。     

模糊系统反向传播学习算法在系统辨识中的应用

文中提出了一种模糊逻辑系统的网络模型，给出了相应的反向传播学习算法，并将其用于非线 辨识，构造了于种动态辨识器。