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Study on some combined forms of fuzzy neural network system and their application on simulation modeling

机译:一些组合形式的模糊和神经网络系统及其对仿真建模的应用研究

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Fuzzyj, neural network (NN) and fuzzy neural network (FNN) technique have been attached more and more importance in increasing research fields. Their application research is also in progress. In the system simulation field, these techniques are similarly studied in theory and applied to practical projects widely. In this paper, four combined forms of NN and fuzzy system are discussed with respect to their merits and demerits. Three FNNs whose consequence is singleton are discussed in details and their learning algorithms are derived. The FNNs' modeling precision is studied by modeling a ballistics model with air resistance. It is concluded that FNN is a better approximator of nonlinear function than NN.
机译:Fuzzyj,神经网络(NN)和模糊神经网络(FNN)技术在增加的研究领域越来越重要。他们的应用研究也正在进行中。在系统仿真领域中,这些技术在理论上类似地研究并应用于实际项目。在本文中,关于其优点和缺点讨论了四种组合形式的NN和模糊系统。三个FNN的后果是单例的详细讨论,并导出了他们的学习算法。通过使用空气阻力模拟弹道学模型研究了FNNS的建模精度。得出结论,FNN是非线性功能的更好近似器。

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