Essays about: "NEURAL CONTROL"

Showing result 21 - 25 of 284 essays containing the words NEURAL CONTROL.

1. 21. Improving the guidance, navigation and control design of the KNATTE platform

   University essay from Luleå tekniska universitet/Institutionen för system- och rymdteknik

   Author : Lars Lundström; [2023]
   Keywords : Multisensor Data Fusion; Nonlinear Kalman Filter; Neural Network;

   Abstract : For complex satellite missions that rely on agile and high-precision manoeuvres, the low-friction aspect of the space environment is a critical component in understanding the attitude control dynamics of the spacecraft. The Kinesthetic Node and Autonomous Table-Top Emulator (KNATTE) is a three-degree-of-freedom frictionless vehicle that serves as the foundation of a multipurpose platform for real-time spacecraft hardware-in-the-loop experiments, and allows emulation of these conditions in two dimensions with the purpose of validating various guidance, navigation, and control algorithms. READ MORE

3. 22. Deep Reinforcement Learning on Social Environment Aware Navigation based on Maps

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Victor Sanchez; [2023]
   Keywords : Deep Reinforcement Learning; Environment-aware navigation; Robotics; Artificial Intelligence; Apprentissage par renforcement profond; Navigation consciente de l’humain; Intelligence Artificielle; Robotique; Djup Förstärkande Inlärning; Människomedveten navigering; Robotik; Artificiell Intelligens;

   Abstract : Reinforcement learning (RL) has seen a fast expansion in recent years of its successful application to a range of decision-making and complex control tasks. Moreover, deep learning offers RL the opportunity to enlarge its spectrum of complex fields. READ MORE

4. 23. Scalable Reinforcement Learning for Formation Control with Collision Avoidance : Localized policy gradient algorithm with continuous state and action space

   University essay from KTH/Skolan för teknikvetenskap (SCI); KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Andreu Matoses Gimenez; [2023]
   Keywords : Control theory; Multi-agent systems; Distributed systems; Formation control; Collision avoidance; Reinforcement learning; Teoria de control; Sistemes multiagent; Sistemes distribuïts; Control de formació; Prevenció de col·lisions; Reinforcement Learning; Reglerteknik; Multi-agent system; Distribuerade system; formationskontroll; Kollisionsundvikande; Reinforcement learning; Teoría de control; Sistemas multiagente; Sistemas distribuidos; Control de formación; Prevención de colisiones; Reinforcement Learning;

   Abstract : In the last decades, significant theoretical advances have been made on the field of distributed mulit-agent control theory. One of the most common systems that can be modelled as multi-agent systems are the so called formation control problems, in which a network of mobile agents is controlled to move towards a desired final formation. READ MORE

5. 24. Fog detection using an artificial neural network

   University essay from Lunds universitet/Matematisk statistik

   Author : Quanwei Li; Tiancheng Ma; [2023]
   Keywords : Machine Learning; Deep Learning; Image Analysis; Computer Vision; Mathematics and Statistics;

   Abstract : This project studies a method of image-based fog detection directly from a camera without using the transmissometer. Fog can be detected using transmissometers which could be a very costly approach. This thesis presents an image-based approach for fog detection using Artificial Neural networks. READ MORE

6. 25. LDPC DropConnect

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Xi Chen; [2023]
   Keywords : Bayesian approach; Machine learning; Coding theory; Measurement uncertainty; Algorithms; Bayesiansk metod; Maskininlärning; Kodningsteori; Mätosäkerhet; Algoritmer;

   Abstract : Machine learning is a popular topic that has become a scientific research tool in many fields. Overfitting is a common challenge in machine learning, where the model fits the training data too well and performs poorly on new data. READ MORE