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1.
多种智能技术的集成与应用   总被引:2,自引:0,他引:2  
当前,人工智能的研究主要分散在专家系统、模糊逻辑、神经网络、进化计算、机器学习与数据挖掘等多个领域。其中专家系统是应用最广的技术之一。但是经典的专家系统存在着“知识瓶颈”问题,严重阻遏专家系统的更深层次的应用。该文旨在运用其他智能技术和专家系统进行结合,构造一种可以进行自动知识获取和整理的新型专家系统,并进一步介绍该理论在笔者构造的基于力信息的运动员训练指导系统中的应用  相似文献   

2.
1.引言专家系统作为人工智能应用研究最活跃和最广泛的课题之一,现已在各个领域取得了很大的成功,其主要组成部分包括知识库、动态数据库、推理机、解释器和接口界面等。知识库存储关于某个领域的专门知识,推理机依据一定的策略进行推理,动态数据库用于存放系统运行过程中所需要的和产生的各种信息,解释器负责解释用户需要了解的一些问题,接口界面则用于人机对话。当前,专家系统的建造主要有以下几种开发环境:一种是专家系统外壳Z二是专家系统开发工具;三是通用人工智能语言;四是通用程序设计语言,如C++、PowerBui…  相似文献   

3.
人工智能作为一门正在迅速发展的学科,已被广泛地应用于传感器领域。人工智能与传感器技术相结合构成新型的智能传感器,提高了传感器的智能水平,是智能传感器发展的一个方向。简要介绍了人工智能的四个分支:模糊逻辑、人工神经网络、专家系统、遗传算法在传感器领域的应用。  相似文献   

4.
抽象思维和形象思维是人类最主要的两种智能形式,因此基于这二种思维形式的智能混合系统成为目前广义人工智能领域内的一个重要研究方向。本文对其研究背景与意义、研究现状与成果以及未来的发展方向作了系统、全面的总结和分析。  相似文献   

5.
专家系统是人工智能领域中解决复杂问题的工具,是人工智能领域中的研究热点之一,。本文设计和实现了一个用于牙科门诊和牙医培训的局部义齿专家系统 。  相似文献   

6.
对人的智能生长过程进行了研究,介绍了人工智能的主要领域——人工神经网络、专家系统和模式识别等相关理论。对国内外人工智能的发展进行了总结,在此基础上提出了生长机器智能理论框架,阐述了生长机器智能的概念及机器智能的生长阶段和预期达到的智能水平。  相似文献   

7.
近几十年来,伴随着我国经济的快速发展,机动车保有量也在快速增加,城市交通问题比如交通拥堵、交通管理效率低等问题日益突出。随着人工智能技术的发展及应用,建立智能交通是解决当前交通问题的有效途径。本文综述了与智能交通相关的人工智能的研究领域,介绍了人工智能在自动驾驶以及解决交通拥堵等交通领域的应用,并探讨了其中可能存在的一些问题和解决方案。  相似文献   

8.
人工智能(Artificial Intelligence),英文缩写为AI.它是研究、开发用于模拟、延伸和扩展人的智能的理论、方法、技术及应用系统的一门新的技术科学.人工智能是计算机科学的一个分支,它企图了解智能的实质,并生产出一种新的能以人类智能相似的方式做出反应的智能机器,该领域的研究包括机器人、语言识别、图像识别、自然语言处理和专家系统等.而机器博弈被认为是人工智能领域最具挑战性的研究方向之一.  相似文献   

9.
专家系统     
人工智能是专门研究如何用机器来承担本来需要人类智能方可完成的工作,探索和模拟人的感觉及思维过程规律的一门学科。具体地说,就是由人来编制计算机程序,让机器来执行本来需要人来完成的任务。故人工智能又称为机器智能,就某种意义上讲,也可以说人工智能就是精巧的计算机软件。人工智能研究所涉及的领域有机器翻译、机器奕棋、模式识别、机器人、定理证明、自然语言理解以及专家系统等,其中专家系统是70年代以来人工智能研究最引人注目的成果。如今,集著名大夫知识和经验于一身的医疗专家系统即“电脑医生”已经开诊服务,专家系统…  相似文献   

10.
近年来,人工智能领域里,专家系统的研究与应用取得了迅速的发展。现在已进入向“专家系统开发工具”攻关的新阶段。三年来,已有几十种专家系统开发工具投放市场,正如费根包姆教授所说:“我们正处在从数据处理向知识处理的转变时期”。“工具系统”是人工智能专家系统的发展或延伸,其目的是提供建立不同专门领域实用专家系统的设计环境。实践表明,应用这种“工具”建立一个实用专家系统要比用LiSp语言设计所用的时间减少一个数量级,又快又方便,从而为各领域专家系统的普遍应用开辟了良好的前景。同时这一发展,必将对信息科学和计算机技术的发展有很大的推动。本文概述与选择专家系统开发工具有关的几个主要技术环节,并简要介绍一些产品的技术特点。  相似文献   

11.
一个基于神经网络的智能评价决策专家系统   总被引:4,自引:0,他引:4  
本文在研究现代智能评价决策专家系统和神经网络的基础上,提出了一种基于神经网络的智能评价决策专家系统,它既能保持专家系统原有特色,又兼有神经网络特点,可以同时获得问题领域中的规范性知识和经验性知识,在实际应用中取得了令人满意的结果。  相似文献   

12.
Nowadays, many current real financial applications have nonlinear and uncertain behaviors which change across the time. Therefore, the need to solve highly nonlinear, time variant problems has been growing rapidly. These problems along with other problems of traditional models caused growing interest in artificial intelligent techniques. In this paper, comparative research review of three famous artificial intelligence techniques, i.e., artificial neural networks, expert systems and hybrid intelligence systems, in financial market has been done. A financial market also has been categorized on three domains: credit evaluation, portfolio management and financial prediction and planning. For each technique, most famous and especially recent researches have been discussed in comparative aspect. Results show that accuracy of these artificial intelligent methods is superior to that of traditional statistical methods in dealing with financial problems, especially regarding nonlinear patterns. However, this outperformance is not absolute.  相似文献   

13.
高峰  李人厚 《信息与控制》1993,22(5):267-275
本文针对自动控制系统中普遍存在的复杂性和不确定性,为弥补传统的基于微分方程、传递函数、状态方程的建模方法的不足,将人工智能思想引入自动控制系统的建模之中,提出了基于谓词形式的自动控制系统智能语言描述方法,采用这种方法,描述自动控制系统简单方便,不仅可以描述常规的控制方法,而且可以描述专家控制、模糊控制和基于神经网络的控制等各种智能控制方法,为分析复杂系统、研究智能控制系统提供了方便。  相似文献   

14.
The paper demonstrates the efficient use of hybrid intelligent systems for solving the classification problem of bankruptcy. The aim of the study is to obtain classification schemes able to predict business failure. Previous attempts to form efficient classifiers for the same problem using intelligent or statistical techniques are discussed throughout the paper. The application of neural logic networks by means of genetic programming is proposed. This is an advantageous approach enabling the interpretation of the network structure through set of expert rules, which is a desirable feature for field experts. These evolutionary neural logic networks are consisted of an innovative hybrid intelligent methodology, by which evolutionary programming techniques are used for obtaining the best possible topology of a neural logic network. The genetic programming process is guided using a context-free grammar and indirect encoding of the neural logic networks into the genetic programming individuals. Indicative classification results are presented and discussed in detail in terms of both, classification accuracy and solution interpretability.  相似文献   

15.
In this paper, the development of a hybrid intelligent system for developing marketing strategy is described. The hybrid system has been developed to: provide a logical process for strategic analysis; support group assessment of strategic marketing factors; help the coupling of strategic analysis with managerial intuition and judgement; help managers deal with uncertainty and fuzziness; and produce intelligent advice on setting marketing strategy. In this system, the strengths of expert systems, fuzzy logic and artificial neural networks (ANNs) are combined to support the process of marketing strategy development. Moreover, the advantages of Porter's five forces model and the directional policy matrices (DPM) are also integrated to assist strategic analysis. In the paper, the software architecture of the hybrid system is discussed in details. Particularly, the group assessment support module, the fuzzification of strategic factors, and the fuzzy reasoning for setting marketing strategy are addressed. In addition, the empirical field work on evaluating the hybrid system is also summarised. The empirical evidence indicates that the hybrid intelligent system is helpful and useful in supporting the development of marketing strategy.  相似文献   

16.
This paper compares the efficiency of two intelligent methods: expert systems and neural networks, in detecting children’s mathematical gift at the fourth grade of elementary school. The input space for the expert system and the neural network model consisted of 60 variables describing five basic components of a child’s mathematical gift identified in previous research. The expert system estimated a child’s gift based on heuristically defined logic rules, while the scientifically confirmed psychological evaluation of gift based on Raven’s standard progressive matrices was used at the output of neural network models. Three neural network algorithms were tested on a Croatian dataset. The results show that both the expert system and the neural network recognize more pupils as mathematically gifted than teachers do. The expert system produces the highest average hit rate, although the highest accuracy in classifying gifted children is obtained by the radial basis neural network algorithm, which also yields lower type II error. Due to the ability of expert systems to explain the result, it can be suggested that both the expert system and the neural network model have potential to serve as effective intelligent decision support tools in detecting mathematical gift in early stage, therefore enabling its further development.  相似文献   

17.
《Applied Soft Computing》2007,7(3):728-738
This work is an attempt to illustrate the utility and effectiveness of soft computing approaches in handling the modeling and control of complex systems. Soft computing research is concerned with the integration of artificial intelligent tools (neural networks, fuzzy technology, evolutionary algorithms, …) in a complementary hybrid framework for solving real world problems. There are several approaches to integrate neural networks and fuzzy logic to form a neuro-fuzzy system. The present work will concentrate on the pioneering neuro-fuzzy system, Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is first used to model non-linear knee-joint dynamics from recorded clinical data. The established model is then used to predict the behavior of the underlying system and for the design and evaluation of various intelligent control strategies.  相似文献   

18.
Knowledge-based modeling and implementation of the various manufacturing processes represent an intensive research area. It is known that it is difficult to analyze the mechanisms of many industrial production processes and build dynamic models by employing classical methods for intelligent systems in manufacturing. This paper describes how to use dynamic recurrent neural networks to provide the model base of a hybrid intelligent system for the metallurgical industry with a quality control model. The hybrid system extracts the features of image sequences obtained through the vision detection subsystem and employs a dynamic recurrent neural network to assess and predict the product qualities to further coordinate the entire production process.  相似文献   

19.
The 90s has seen the emergence of hybrid configurations of four most commonly used intelligent methodologies, namely, symbolic knowledge based systems (e.g. expert systems), artificial neural networks, fuzzy systems and genetic algorithms. These hybrid configurations are used for different problem solving tasks/situations. In this paper we describe unified problem modeling language at two different levels, the task structure level for knowledge engineering of complex data intensive domains, and the computational level of the task level hybrid architecture. Among other aspects, the unified problem modeling language considers various intelligent methodologies and their hybrid configurations as technological primitives used to accomplish various tasks defined at the task structure level. The unified problem modeling language is defined in the form of five problem solving adapters. The problem solving adapters outline the goals, tasks, percepts/inputs, and hard and soft computing methods for modeling complex problems. The task structure level has been applied in modeling several applications in e-commerce, image processing, diagnosis, and other complex, time critical, and data intensive domains. We also define a layered intelligent multi-agent, operating system processes, intelligent technologies with the task structure level associative hybrid architecture. The layered architecture also facilitates component based software modeling process.Work Supported by VPAC grant no EPPNLA002.2001  相似文献   

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