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Backpropagation Neural Tree [PDF]
Neural Networks, 2022 We propose a novel algorithm called Backpropagation Neural Tree (BNeuralT), which is a stochastic computational dendritic tree. BNeuralT takes random repeated inputs through its leaves and imposes dendritic nonlinearities through its internal connections like a biological dendritic tree would do. Considering the dendritic-tree like plausible biological
Varun Ojha, Giuseppe Nicosia
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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2022 Garbage is a problem that needs an in-depth study in urban areas because the development of an area has consequences on increasing population density, facilities and infrastructure, public services, and other aspects that impact increasing the volume of ...
Luqman Hakim +4 more
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Memorized sparse backpropagation [PDF]
Neurocomputing, 2020 Accepted to ...
Zhang, Zhiyuan +4 more
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INFLATION FORECASTS IN AMBON USING NEURAL NETWORK APPLICATIONS BACKPROPAGATION
Barekeng, 2022 An artificial Neural Network is the processing of information systems on certain characteristics which are artificial representations based on human neural networks.
Mozart W Talakua +3 more
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APPLICATION OF THE BACKPROPAGATION METHOD TO PREDICT RAINFALL IN NORTH SUMATRA PROVINCE
Barekeng, 2023 Natural disasters are to blame for the high level of community loss. This is due to the community's lack of information about potential disasters around them. As a result, public understanding of disaster response is extremely low.
Rinjani Cyra Nabila +3 more
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Image Analysis of Diabetic Retinopathy Disease Based on Artificial Neural Network Algorithms
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2020 Diabetic retinopathy is a complication of diabetes in the form of damage to the retina of the eye. High levels of glucose in the blood are the cause of small capillary blood vessels to rupture and can cause blindness.
Tri Astuti, Gesha Agus Setiawan
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Real-time stability assessment in smart cyber-physical grids: a deep learning approach
IET Smart Grid, 2020 The increasing coupling between the physical and communication layers in the cyber-physical system (CPS) brings up new challenges in system monitoring and control.
Farzad Darbandi +6 more
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Learning cortical hierarchies with temporal Hebbian updates
Frontiers in Computational Neuroscience, 2023 A key driver of mammalian intelligence is the ability to represent incoming sensory information across multiple abstraction levels. For example, in the visual ventral stream, incoming signals are first represented as low-level edge filters and then ...
Pau Vilimelis Aceituno +5 more
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Advanced Intelligent Systems, 2021 On‐chip training of neural networks (NNs) is regarded as a promising training method for neuromorphic systems with analog synaptic devices. Herein, a novel on‐chip training method called direct gradient calculation (DGC) is proposed to substitute ...
Hyungyo Kim +7 more
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Barekeng, 2020 Backpropagation Artificial Neural Network (ANN) is an ANN that uses a supervised learning algorithm. The purpose of this study is to determine the parameters and measure the accuracy of the classification accuracy of the student status of the Open ...
Siti Hadijah Hasanah +1 more
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