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Active noise control in free space using recurrent neural networks with EKF algorithm
Bambang R.T.a, Uchida K.b, Yacoub R.R.a
a School of Electrical Engineering and Informatics, Bandung Institute of Technology, Indonesia
b Department Electrical Engineering, Waseda University, Japan
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]This paper presents theoretical and experimental investigation of active noise control (ANC) in free space using recurrent neural networks. A learning algorithm for diagonal recurrent neural networks based on extended Kalman filter is developed and is referred to as diagonal recurrent extended Kalman filter (DREKF) algorithm. Based on DREKF, new control algorithm suited for ANC is developed to handle nonlinearity inherently arising in this application. Real-time experiment using floating point digital signal processor is carried out for both identification and control tasks required in ANC. The results show that the number of neurons in neural network can be reduced by introducing the diagonal recurrent elements, without deteriorating the system performance, and that DREKF produces better performance than linear adaptive controller in compensating the secondary path nonlinearity.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Active noise control (ANC),Adaptive controllers,Con trol algorithms,Control tasks,Diagonal recurrent neural networks,Digital signal processor,Digital signals,Experimental investigations,Extended Kalman filter,Floating point,Free space,Free spaces,Noise controlled,Non linearities,Nonlinearity,Secondary path nonlinearity,System performances[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Active noise control,Diagonal recurrent neural networks,Digital signal processor,Extended Kalman filter,Free space,Nonlinearity[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]