Frontiers

Formal Verification of Neural Networks-Based Control Architecture for Safety-Critical Autonomous Systems

With increase in the applications of autonomous systems, in both civilian and military domains, it has become increasingly ...
Deep Learning with Yacine on MSN

Deep Neural Network from Scratch in Python – Fully Connected Feedforward Tutorial

Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory ...
Deep Learning with Yacine on MSN

Understanding Forward Propagation in Neural Networks with Python – Step by Step

Learn how forward propagation works in neural networks using Python! This tutorial explains the process of passing inputs ...
EE World Online

How does deep learning actually work?

This FAQ explores the fundamental architecture of neural networks, the two-phase learning process that optimizes millions of ...
eLife

Neural Representation of Associative Threat Learning in Pulvinar Divisions, Lateral Geniculate Nucleus, and Mediodorsal Thalamus in Humans

Understanding the neural mechanisms underlying associative threat learning is essential for advancing behavioral models of threat and adaptation. We investigated distinct activation patterns across ...
IEEE

A Parallel GRU-Transformer Neural Network Approach for Feedforward Compensation in Precision Motion Platform Control

Abstract: This paper proposes a feedforward compensation strategy based on Parallel GRU-Transformer neural network to address the issues of large tracking errors and insufficient stability of multi ...
Scientific Research Publishing

Deformation Control and Early Warning Analysis of Deep Riverside Foundation Pit Construction Process Based on Machine Learning ()

1 China Communications Third Shipping Engineering Bureau Co., Ltd., Shanghai, China. 2 School of Civil Engineering, Wuhan University, Wuhan, China. The large deep foundation pit projects usually face ...
IEEE

Adaptive Output Feedback Control Using Lyapunov-Based Deep Recurrent Neural Networks (Lb-DRNNs)

Abstract: Unlike traditional feedforward neural networks, recurrent neural networks (RNNs) possess a recurrent connection that allows them to retain past information. This internal memory enables RNNs ...
Frontiers

Deriving equivalent symbol-based decision models from feedforward neural networks

1 KNDS Deutschland GmbH & Co. KG, Munich, Germany 2 Institute for Software Technology, University of the Bundeswehr Munich, Neubiberg, Germany Artificial intelligence (AI) has emerged as a ...