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基于函数论的立场,指出模糊推理过程是一个函数变换过程,模糊规则蕴涵了一个从函数空间到函数空间的映射,现存的种种模糊推理方法都是对这种映射的估计,进而指出插值和回归的方法都适用于这种估计。系统地提出了用回归的方法处理模糊推理的思想,并结俣线性回归模型进行了示范,证明了基于线性回归模型的模糊推理系统(FIS)同样是一个万能函数逼近器。 相似文献
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模糊系统是一种基于知识或基于规则的系统,它的核心就是由所谓的IF-THEN规则所组成的知识库.模糊推理就是针对给定的系统输入,综合运用知识库中的模糊推理规则,获得系统输出的过程.而T-S模糊模型的基本思想是将正常的模糊规则及其推理转换成一种数学表达形式.本文拟将绩效考核与模糊推理的优越性进行有效的结合,研究讨论出T-S模糊推理在绩效考核中的应用.以验证其收敛性及优越性. 相似文献
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自适应神经网络模糊推理系统最优参数的研究 总被引:1,自引:0,他引:1
模糊规则的提取和隶属度函数的学习是模糊系统设计中重要而困难的问题。自适应神经网络模糊推理系统(ANFIS)能基于数据建模,无须专家经验,自动产生模糊规则和调整隶属度函数。在建立一个初始系统进行训练时,其隶属度函数的类型、隶属度函数的数日以及训练次数都是待定的,这三个参数的选择直接影响系统训练后的效果,它们的确定方法有待研究。该文应用自适应神经网络模糊推理系统的方法对一个典型系统进行建模仿真,并阐述这三个参数的寻优方法。 相似文献
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1 引言模糊推理是一种利用模糊集合论研究不确定性问题的方法。实际上多输入多输出系统的推理问题的关键是单输入单输出系统的模糊推理问题,因此本文只考虑单输入单输出系统的模糊推理问题。模糊推理中涉及到两个主要的问题:一是模糊蕴涵算子的选择,二是推理合成方式的选择。模糊蕴涵算子将规则转换成 相似文献
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基于信息分配技术的模糊推理方法 总被引:1,自引:0,他引:1
模糊推理是人工智能中的一个重要研究内容;信息分配技术是在信息不足情况下进行推理的有效而又简单的方法。本文讨论了基于信息分配技术的模糊推理方法,并用实例介绍了信息分配技术的应用。此类方法可广泛应用于人工智能和专家系统中。 相似文献
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本文提出一种规则结论部分的语言变量具有离散隶属度函数的、基于Mamdani形规则的新神经模糊系统,并描述了它的学习算法.新神经模糊系统由模糊推理系统及其一一对应的神经网络系统构成.在只有训练数据的情况下,首先提出了一种基于RBF神经网络的模糊建模方法.而在模糊推理系统由模糊建模或者直接由专家经验知识确定后,应用梯度下降法优化神经网络系统参数.倒立摆控制和时间序列预测的仿真试验体现了本文提出的新的神经模糊系统的可用性和优越性. 相似文献
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通过分析机电设备的故障机理,运用模糊理论及其推理方法设计了一种机电设备故障诊断模糊推理机。重点对基于模糊数学理论和模糊推理方法的推理机制进行了研究,该推理机能够有效处理由于知识的模糊性所引起的不确定性问题。 相似文献
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A fuzzy reasoning design for fault detection and diagnosis of a computer-controlled system 总被引:1,自引:0,他引:1
Y. Ting W.B. Lu C.H. Chen G.K. Wang 《Engineering Applications of Artificial Intelligence》2008,21(2):157-170
A fuzzy reasoning and verification Petri nets (FRVPNs) model is established for an error detection and diagnosis mechanism applied to a complex fault-tolerant PC-controlled system. The inference accuracy can be improved through the hierarchical design of a two-level fuzzy rule decision tree and a Petri nets technique to transform the fuzzy rule into the FRVPNs model. Several simulation examples of the assumed failure events were carried out by using the FRVPNs and the Mamdani fuzzy method with MATLAB tools. The reasoning performance of the developed FRVPNs was verified by comparing the inference outcome to that of the Mamdani method. Both methods result in the same conclusions. Thus, the present study demonstrates that the proposed FRVPNs model is able to achieve the purpose of reasoning, and furthermore, determining of the failure event of the monitored application program. 相似文献
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邓九英 《计算机工程与应用》2003,39(19):136-138
该文提出一种具有修正因子的自调整模糊控制器软件仿真系统RMDFCSS。它包含有编辑器、调试器、模糊推理机和代码生成器功能,用编辑器定义模糊规则、隶属函数、特定的推理方法和反模糊化方法;用调试器能检查整个推理过程的每一个步骤,完成模糊控制算法模型的确立、论证和优化操作。 相似文献
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神经模糊系统在机器人的智能控制中具有巨大的应用潜力,但已有的系统构造方法几乎都面临着样本资源匮乏这一巨大困难。为克服传统系统构造方法可能因样本获取困难而引起的“维数灾难”等问题,该文在模糊神经网络中引入了Q-学习机制,提出了一种基于Q-学习的模糊神经网络模型,从而赋予神经模糊系统自学习能力。文章最后给出了其在菅野模糊小车控制中的仿真结果。实验表明,在神经模糊系统中融入智能学习机制Q-学习是行之有效的;它可以被用来实现机器人智能行为的自学习。值得一提的是,该文的仿真实验在真实系统上同样是容易实现的,只要系统能提供作为评价信号的传感信息即可。 相似文献
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M. Setnes H.R. van Nauta Lemke U. KaymakAuthor vitae 《Engineering Applications of Artificial Intelligence》1998,11(6):781-789
FAIR (fuzzy arithmetic-based interpolative reasoning)—a fuzzy reasoning scheme based on fuzzy arithmetic, is presented here. Linguistic rules of the Mamdani type, with fuzzy numbers as consequents, are used in an inference mechanism similar to that of a Takagi–Sugeno model. The inference result is a weighted sum of fuzzy numbers, calculated by means of the extension principle. Both fuzzy and crisp inputs and outputs can be used, and the chaining of rule bases is supported without increasing the spread of the output fuzzy sets in each step. This provides a setting for modeling dynamic fuzzy systems using fuzzy recursion. The matching in the rule antecedents is done by means of a compatibility measure that can be selected to suit the application at hand. Different compatibility measures can be used for different antecedent variables, and reasoning with sparse rule bases is supported. The application of FAIR to the modeling of a nonlinear dynamic system based on a combination of knowledge-driven and data-driven approaches is presented as an example. 相似文献
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The ability to accurately and consistently estimate software development efforts is required by the project managers in planning
and conducting software development activities. Since software effort drivers are vague and uncertain, software effort estimates,
especially in the early stages of the development life cycle, are prone to a certain degree of estimation errors. A software
effort estimation model which adopts a fuzzy inference method provides a solution to fit the uncertain and vague properties
of software effort drivers. The present paper proposes a fuzzy neural network (FNN) approach for embedding artificial neural
network into fuzzy inference processes in order to derive the software effort estimates. Artificial neural network is utilized
to determine the significant fuzzy rules in fuzzy inference processes. We demonstrated our approach by using the 63 historical
project data in the well-known COCOMO model. Empirical results showed that applying FNN for software effort estimates resulted
in slightly smaller mean magnitude of relative error (MMRE) and probability of a project having a relative error of less than or equal to 0.25 (Pred(0.25)) as compared with the results obtained by just using artificial neural network and the original model. The proposed
model can also provide objective fuzzy effort estimation rule sets by adopting the learning mechanism of the artificial neural
network. 相似文献
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