This post is inspired by http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch. In this post, we will implement a multiple layer neural network ...

Abstract: This paper describes a channel estimation and equalization algorithm using three-layer artificial neural networks (ANNs) with feedback for multiple input multiple output wireless ...

The goal of this project was to analyze how hidden layers, neurons, and regularization affect neural network model performance. Two datasets of varying sizes ... The model in the experiment had only 1 ...

Merging neurons with semiconductors signals a shift in brain–machine interface design and neuromorphic engineering.

Based on the deep learning method, the convolutional neural network model is used to extract the newly produced 3D feature to ... the plane and angle of a 3D object associated with different ...

a) Schematic ... programmed multiple distinct memory levels. They recorded 19 discrete storage states under low-voltage conditions, enabling multi-bit memory in a single unit. The readout current ...

In recent years, ML has become dominated by deep learning (DL), which is a methodology using multilayer neural networks to progressively obtain more abstract representations of complex data. Figure 1 ...

(7,8) The limited theoretical modeling performance may hamper the quality of the predicted behavior of chemical systems consisting of multiple reactions. The results found provide ... Despite training ...