[vc_empty_space][vc_empty_space]
Determining the neural network topology: A review
Muh Ibnu Choldun R.a, Santoso J.a, Surendro K.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
[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]© 2019 Association for Computing Machinery.One of the challenges in the successful implementation of deep neural network (DNN) is setting the value for various hyperparameters, one of which is the network topology, which is closely related to the number of hidden layers and the number of hidden neurons. Determining the number of hidden layers and the number of neurons is very important and has a large influence on DNN performance. Determining these two numbers manually (usually through trial and error methods) to find fairly optimal arrangement is a time-consuming process, while the automatic approach is divided into two, they are a model-based approach and a non-model based approach. The non-model-based approach, for example, is grid search and random search, whereas model-based approaches, for example, are using particle swarm optimization (PSO) algorithms. In some researches, how to determine the number of hidden layers or number of neurons, often the guidelines are unclear, even the roles and functions of both are explained minimally. Although it is still a difficult area of research, research to determine the number of hidden layers and the number of neurons must continue to be carried out, because these two numbers will greatly determine the performance of DNN.[/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]Hidden layers,Hidden neurons,Model based approach,Neural network topology,Number,Number of hidden neurons,Particle swarm optimization algorithm,Trial-and-error method[/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]Hidden layer,Hidden neuron,Neural network,Number[/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]https://doi.org/10.1145/3316615.3316697[/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]