Sökning: "RBF Networks"
Visar resultat 1 - 5 av 6 uppsatser innehållade orden RBF Networks.
1. Transformer-Based Multi-scale Technical Reports Analyser for Science Projects Cost Prediction
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Intrinsic value prediction is a Natural Language Processing (NLP) problem consisting in determining a numerical value contained implicitly and non-trivially in a text. In this project, we introduce the SWORDSMAN model (Sentence and Word-level Oracle for Research Documents by Semantic Multi-scale ANalysis), a deep neural network architecture based on transformers whose goal is to predict the cost of research projects from the analysis of their abstract. LÄS MER
2. Blackhole Attack Detection in Low-Power IoT Mesh Networks Using Machine Learning Algorithms
Master-uppsats, Blekinge Tekniska Högskola/Institutionen för datavetenskapSammanfattning : Low-Power Lossy Networks (LLNs) are a type of Internet of Things (IoT) meshnetwork that collaboratively interact and perform various tasks autonomously. TheRouting Protocol for Low-power and Lossy Network (RPL) is the most used rout-ing protocol for LLNs. LÄS MER
3. Classification of Flying Qualities with Machine Learning Methods
Master-uppsats, KTH/FlygdynamikSammanfattning : The primary objective of this thesis is to evaluate the prospect of machine learning methods being used to classify flying qualities based on simulator data (with the focus being on pitch maneuvers). If critical flying qualities could be identified earlier in the verification process, they can be further invested in and focused on with less cost for design changes of the flight control system. LÄS MER
4. Dynamic Speed Adaptation for Curves using Machine Learning
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : The vehicles of tomorrow will be more sophisticated, intelligent and safe than the vehicles of today. The future is leaning towards fully autonomous vehicles. LÄS MER
5. Investigating the use of multi-label classification methods for the purpose of classifying electromyographic signals
Master-uppsats, Lunds universitet/Avdelningen för Biomedicinsk teknikSammanfattning : The type of pattern recognition methods used for controlling modern prosthetics, referred to here as single-label classification methods, restricts users to a small amount of movements. One prominent reason for this is that the accuracy of these classification methods decreases as the number of allowed movements is increased. LÄS MER
