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首页> 外文期刊>IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics >Tuning certainty factor and local weight of fuzzy production rulesby using fuzzy neural network
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Tuning certainty factor and local weight of fuzzy production rulesby using fuzzy neural network

机译：用模糊神经网络调整模糊生产规则的确定性因子和局部权重

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摘要

Approximate reasoning in a fuzzy system is concerned withninferring an approximate conclusion from fuzzy and vague inputs. Therenare many ways in which different forms of conclusions can be drawn.nFuzzy sets are usually represented by fuzzy membership functions. Thesenmembership functions are assumed to have a clearly defined base. Fornother fuzzy sets such as intelligent, smart, or beautiful, etc., itnwould be difficult to define clearly its base because its base maynconsist of several other fuzzy sets or unclear nonfuzzy bases. A methodnto handle this kind of fuzzy set is proposed. A fuzzy neural networkn(FNN) is also proposed to tune knowledge representation parametersn(KRPs). The contributions are that we are able to handle a broader rangenof fuzzy sets and build more powerful fuzzy systems so that thenconclusions drawn are more meaningful, reliable, and accurate. Annexperiment is presented to demonstrate how our method works
机译：模糊系统中的近似推理与从模糊和模糊输入中推断出近似结论有关。有很多方法可以得出不同形式的结论。n模糊集通常由模糊隶属度函数表示。假定成员功能具有明确定义的基础。对于诸如智能，灵巧或漂亮等其他模糊集，将很难清晰地定义其基数，因为其基数可能不包括其他几个模糊集或不清楚的非模糊基数。提出了一种处理这种模糊集的方法。还提出了一种模糊神经网络（FNN）来调整知识表示参数n（KRPs）。所做的贡献是，我们能够处理范围更广的模糊集并构建更强大的模糊系统，从而得出的结论更加有意义，可靠和准确。附件附件展示了我们的方法的工作原理

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