Compression driver design involves the study of complex mathematical models characterized by a great number of variables, implying high computational cost and long design time. Therefore, an optimization procedure is required to enhance the design procedure, especially from the parameters point of view. In this paper, a combined approach based both on evolution strategy procedure and neural network model is presented. Taking into consideration several tests on a real compression driver, the proposed method is capable to enhance the design procedure from the point of view of obtained frequency response and of the computational performance.
Combining evolution strategies and neural network procedures for compression driver design / Gasparini, Michele; Vesperini, Fabio; Cecchi, Stefania; Squartini, Stefano; Piazza, Francesco; Toppi, Romolo. - ELETTRONICO. - (2016), pp. 3385-3390. (Intervento presentato al convegno IJCNN 2016 tenutosi a Vancouver, Canada nel 24-29 July 2016) [10.1109/IJCNN.2016.7727632].
Combining evolution strategies and neural network procedures for compression driver design
GASPARINI, MICHELE;VESPERINI, FABIO;CECCHI, STEFANIA;SQUARTINI, Stefano;PIAZZA, Francesco;
2016-01-01
Abstract
Compression driver design involves the study of complex mathematical models characterized by a great number of variables, implying high computational cost and long design time. Therefore, an optimization procedure is required to enhance the design procedure, especially from the parameters point of view. In this paper, a combined approach based both on evolution strategy procedure and neural network model is presented. Taking into consideration several tests on a real compression driver, the proposed method is capable to enhance the design procedure from the point of view of obtained frequency response and of the computational performance.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.