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Fuzzy logic has been used as a means of interpreting vague, incomplete and even contradictory information into a compromised rule base in artificial intelligence such as machine decision–making. Within this context, fuzzy logic can be applied in the field of expert systems to provide additional flexibilities in constructing a working rule base: different experts' opinions can be incorporated into the same rule base, and each opinion can be modeled in a rather vague notion of human language. As some illustrative application examples, this paper describes how fuzzy logic can be used in expert systems. More precisely, it demonstrates the following applications: (i) a healthcare diagnostic system, (ii) an autofocus camera lens system and (iii) a financial decision system. For each application, basic rules are described, the calculation method is outlined and numerical simulation is provided. These applications demonstrate the suitability and performance of fuzzy logic in expert systems. 相似文献
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In this paper, we propose an Interactive Fuzzy Interval Reasoning (FIR) method by combining fuzzy logic with interval computing to better serve Web users in terms of effectiveness and flexibility. Web users may use convenient interval inputs for online shopping. In order to serve different customers based on their preferences, different personalized fuzzy partitions to meet different needs are provided for the different Web customers. The Interactive Fuzzy Interval Reasoning method is used to design the Web shopping agent. Java servlets and Microsoft Access are used to implement the fuzzy Web shopping system. 相似文献
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In this paper we present a method for response integration in multi-net neural systems using interval type-2 fuzzy logic and fuzzy integrals, with the purpose of improving the performance in the solution of problems with a great volume of information. The method can be generalized for pattern recognition and prediction problems, but in this work we show the implementation and tests of the method applied to the face recognition problem using modular neural networks. In the application we use two interval type-2 fuzzy inference systems (IT2-FIS); the first IT2-FIS was used for feature extraction in the training data, and the second one to estimate the relevance of the modules in the multi-net system. Fuzzy logic is shown to be a tool that can help improve the results of a neural system by facilitating the representation of human perceptions. 相似文献
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Many phenomena in our lives are difficult to predict. Especially financial markets have eluded successful prediction attempts. Interest rates are quite volatile and nonlinear. We develop the system capable of processing Korean financial data and modeling time-series processes (such as interest rate) with fuzzy logic and genetic algorithms(GAs). In this paper, we bring together two technologies: fuzzy theory and genetic algorithms. The combination of these techniques could be applied to the interest rate forecasting problem in Korean financial market. The fuzzy rules can be concisely represented with one or more FAM (Fuzzy Associative Memory) matrices. We use GAs to adapt the FAM matrix entries so that the interest rate forecasting problem leads to an improved performance. This paper presents the Genetic-Based Fuzzy Model (GBFM). 相似文献
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Systematic design of a stable type-2 fuzzy logic controller 总被引:1,自引:0,他引:1
Stability is one of the more important aspects in the traditional knowledge of automatic control. Type-2 fuzzy logic is an emerging and promising area for achieving intelligent control (in this case, fuzzy control). In this work we use the fuzzy Lyapunov synthesis as proposed by Margaliot and Langholz [M. Margaliot, G. Langholz, New Approaches to Fuzzy Modeling and Control: Design and Analysis, World Scientific, Singapore, 2000] to build a Lyapunov stable type-1 fuzzy logic control system, and then we make an extension from a type-1 to a type-2 fuzzy logic control system, ensuring the stability on the control system and proving the robustness of the corresponding fuzzy controller. 相似文献
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关联规则是数据库中的知识发现(KDD)领域的重要研究课题。模糊关联规则可以用自然语言来表达人类知识,近年来受到KDD研究人员的普遍关注。但是,目前大多数模糊关联规则发现方法仍然沿用经典关联规则发现中常用的支持度和置信度测度。事实上,模糊关联规则可以有不同的解释,而且不同的解释对规则发现方法有很大影响。从逻辑的观点出发,定义了模糊逻辑规则、支持度、蕴含度及其相关概念,提出了模糊逻辑规则发现算法,该算法结合了模糊逻辑概念和Apriori算法,从给定的定量数据库中发现模糊逻辑规则。 相似文献
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心电图的智能识别技术 总被引:4,自引:0,他引:4
模糊逻辑、神经网络是人工智能的重要分支,它们从不同角度、在一定程度上模拟了人类智能。本文先后将模糊逻辑、神经网络以及模糊神经网络技术用于心电图识别,获得了良好的效果。在模糊识别方面,从模糊识别矩阵的建立到模糊输入向量的确定,是针对此类具体问题的多传感器模糊信息融合算法,既综合考虑了各输入变量的作用,又突出了识别的主要依据。本文还给出了神经网络识别的三种试验结果及其与模糊神经网络识别的对比。模糊神经网络既充分发挥了神经网络的学习功能,又充分发挥了模糊逻辑的推理功能,因此具有很高的识别精度。 相似文献
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Time–cost tradeoff (TCT) problem in project scheduling studies how to schedule project activities to achieve a tradeoff between project cost and project completion time. It gives project planners both challenges and opportunities to work out the best plan that optimizes time and cost to complete a project. In this paper, we present a novel method which examines the effects of project uncertainties on both, the duration as well as the cost of the activities. This method integrates a fuzzy logic framework with Hybrid Meta-Heuristic. Hybrid Meta-Heuristic (HMH) is an innovative approach which hybridizes a multiobjective genetic algorithm and simulated annealing. Integration of HMH and fuzzy logic is referred to as ‘integrated Fuzzy–HMH’. A rule based fuzzy logic framework brings up changes in the duration and the cost of each activity for the input uncertainties and HMH searches for Pareto-optimal front (TCT profile) for a given set of time–cost pair of each project activity. Two standard test problems from the literature are attempted using HMH. A case study of TCT problem is solved using integrated Fuzzy–HMH. The method solves time–cost tradeoff problems within an uncertain environment and carries out its sensitivity analysis. 相似文献
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FLcom是建立在模糊集FScom基础上的一种区分矛盾否定、对立否定和中介否定的模糊命题逻辑形式系统。在模糊推理中关于否定的认识和处理主要以经典逻辑为基础,为此在FLcom基础上研究了区分3种否定的模糊推理规则的表示,给出了基于FLcom的模糊推理规则的合成算法FLMP和FLMT规则,新算法推广了CRI算法中的蕴涵算子,并给出了模糊推理应用的实例对比。结果表明FLcom在区分不同否定的实际应用中是合理可行的。 相似文献
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In this paper, a fuzzy inference network model for search strategy using neural logic network is presented. The model describes search strategy, and neural logic network is used to search. Fuzzy logic can bring about appropriate inference results by ignoring some information in the reasoning process. Neural logic networks are powerful tools for the reasoning process but not appropriate for the logical reasoning. To model human knowledge, besides the reasoning process capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct a fuzzy inference network model based on the neural logic network, extending the existing rule inference network. And the traditional propagation rule is modified. 相似文献
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模糊数学已经被大量地应用于工业控制,本文根据证券市场上投资者的思维过程与模糊逻辑的相似性,尝试把模糊学引入股票市场投机行为的控制,模拟股票市场上投资者的股票买卖行为,旨在探索一种基于模糊逻辑的自动股票投机和投资逻辑,可以为股票软件开发提供理论依据和逻辑模型,建立在模糊逻辑基础上的股票软件可以引导投资者避免追涨杀跌,从而有利于股市稳定而健康地发展. 相似文献
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模糊逻辑是人脑思维活动的基本方式,而神经网络则是模仿人脑神经系统功能而设计的一类巨型非线性网络。所以将模糊逻辑与神经网络相结合具有很大的前途。本文利用模糊神经元突破了没有计算机就不能实现模糊控制的传统观点,给出了一种不用计算机就能实现的模糊神经网络控制器,对模糊控制的硬件实现起到积极的作用。 相似文献
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刘富春 《计算机工程与应用》2006,42(2):73-75,175
模糊集与模糊逻辑是处理大量存在的不确定性与模糊性信息的重要数学工具,在近似推理等领域有着广泛的应用。该文将王家兵等人提出的真值取在[0,1]区间上的带有相似性关系的模糊逻辑,扩充到很一般的与滋可比的有余完全分配格值逻辑中,将王家兵等人的许多结论进行了推广。首先对带有相似性关系的模糊逻辑的语义描述进行了扩充,然后讨论了在这种模糊推理中归结式与调解式的有效性,最后通过证明一个子句集在扩充模糊逻辑中的不可满足性与它在带有相等关系的二值逻辑中的不可满足性是等价的,得到了基于归结与调解方法对这种广义模糊演算的完备性。 相似文献
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In order to analyze the logical foundation of fuzzy reasoning, this paper first introduces the concept of generalized roots of theories in ?ukasiewicz propositional fuzzy logic ?uk, Gödel propositional fuzzy logic Göd, Product propositional fuzzy logic Π, and nilpotent minimum logic NM (the R0-propositional fuzzy logic L∗). Next, it is proved that all consequences of a theory Γ, named D(Γ), are completely determined by its generalized root whenever Γ has a generalized root. Moreover, it is proved that every finite theory Γ has a generalized root, which can be expressed by a specific formula. Finally, we demonstrate the existence of a non-fuzzy version of Fuzzy Modus Ponens (FMP) in ?uk, Göd, Π and NM (L∗), and we provide its numerical version as a new algorithm for solving FMP. 相似文献