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

Abstract

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 developed 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.