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Modelling and simulation with neural and fuzzy-neural networks of switched circuits

机译：开关电路的神经网络和模糊神经网络的建模与仿真

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摘要

Recently, the modelling and simulation of switched systems containing new nonlinear components in electronics and power electronics industry have gained importance. In this paper, both feed-forward artificial neural networks (ANN) and adaptive network-based fuzzy inference systems (ANFIS) have been applied to switched circuits and systems. Then their performances have been compared in this contribution by devebped simulation programs. It has been shown that ANFIS require less training time and offer better performance than those of ANN. In addition, ANFIS using "clustering algorithm" to generate the rules and the numbers of membership functions gives a smaller number of parameters, better performance and less training time than those of ANFIS using "grid partition" to generate the rules. The work not only demonstrates the advantage of the ANFIS architecture using clustering algorithm but also highlights the advantages of the architecture for hardware realizations.
机译：最近，在电子和电力电子行业中，包含新的非线性组件的开关系统的建模和仿真已变得越来越重要。在本文中，前馈人工神经网络（ANN）和基于自适应网络的模糊推理系统（ANFIS）均已应用于开关电路和系统。然后，通过精湛的仿真程序比较了他们的表现。结果表明，与ANN相比，ANFIS需要更少的培训时间并提供更好的性能。另外，与使用“网格划分”生成规则的ANFIS相比，使用“聚类算法”生成规则的ANFIS和隶属函数的数目提供了更少的参数，更好的性能和更少的训练时间。这项工作不仅展示了使用聚类算法的ANFIS架构的优势，而且还突出了该架构在硬件实现方面的优势。

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