| Additive—Multiplicative Fuzzy Neural Network and Its Performance |
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| 引用本文： | 翟东海,靳蕃.Additive—Multiplicative Fuzzy Neural Network and Its Performance[J].西南交通大学学报(英文版),2003,11(1):16-22. |
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| 作者姓名： | 翟东海 靳蕃 |
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| 作者单位： | School of Computer and Communication Engineering，Southwest Jiaotong University，School of Computer and Communication Engineering，Southwest Jiaotong University Chengdu 610031，China，Chengdu 610031，China |
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| 摘 要： | Introduction Kosko1] hasprovedthatanadditivefuzzysystemcanapproximateanycontinuousfunctiononacompactdomaintoarbitraryaccuracy .AspointedbyWang2 ] ,amultiplicativefuzzysystem ,whichisconstructedbyusingGaussianmembershipfunctionandcentroiddefuzzificati…
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| 关 键 词： | 模糊神经网络 模糊规则 AMFNN 模糊数学 模糊推论 |
Additive-Multiplicative Fuzzy Neural Network and Its Performance |
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| Zhai DonghaiJin Fan.Additive-Multiplicative Fuzzy Neural Network and Its Performance[J].Journal of Southwest Jiaotong University,2003,11(1):16-22. |
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| Authors: | Zhai DonghaiJin Fan |
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| Abstract: | In view of the main weaknesses of current fuzzy neural networks such as low reasoning precision and long training time, an Additive Multiplicative Fuzzy Neural Network (AMFNN) model and its architecture are presented. AMFNN combines additive inference and multiplicative inference into an integral whole, reasonably makes use of their advantages of inference and effectively overcomes their weaknesses when they are used for inference separately. Here, an error back propagation algorithm for AMFNN is presented based on the gradient descent method. Comparisons between the AMFNN and six representative fuzzy inference methods shows that the AMFNN is characterized by higher reasoning precision, wider application scope, stronger generalization capability and easier implementation. |
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| Keywords: | fuzzy inference additive multiplicative fuzzy neural network fuzzy rule acquisition |
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