O presente trabalho propõe resolver o clássico problema combinatorial conhecido como problema do caixeiro viajante. Foi usado no sistema de otimização de busca do menor caminho uma rede neural recorrente. A topologia de estrutura de ligação das realimentações da rede adotada aqui é conhecida por rede recorrente de Wang. Como regra de treinamento de seus pesos sinápticos foi adotada a técnica de perturbação simultânea com aproximação estocástica. Foi elaborado ainda uma minuciosa revisão bibliográfica sobre todos os temas abordados com detalhes sobre a otimização multivariável com perturbação simultânea. Comparar-se-á também os resultados obtidos aqui com outras diferentes técnicas aplicadas no problema do caixeiro viajante visando propósitos de validação. / This work proposes to solve the classic combinatorial optimization problem known as traveling salesman problem. A recurrent neural network was used in the system of optimization to search the shorter path. The structural topology linking the feedbacks of the network adopted here is known by Wang recurrent network. As learning rule to find the appropriate values of the weights was used the simultaneous perturbation with stochastic approximation. A detailed bibliographical revision on multivariable optimization with simultaneous perturbation is also described. Comparative results with other different techniques applied to the traveling salesman are still presented for validation purposes.

O presente trabalho propõe resolver o clássico problema combinatorial conhecido como problema do caixeiro viajante. Foi usado no sistema de otimização de busca do menor caminho uma rede neural recorrente. A topologia de estrutura de ligação das realimentações da rede adotada aqui é conhecida por rede recorrente de Wang. Como regra de treinamento de seus pesos sinápticos foi adotada a técnica de perturbação simultânea com aproximação estocástica. Foi elaborado ainda uma minuciosa revisão bibliográfica sobre todos os temas abordados com detalhes sobre a otimização multivariável com perturbação simultânea. Comparar-se-á também os resultados obtidos aqui com outras diferentes técnicas aplicadas no problema do caixeiro viajante visando propósitos de validação. / This work proposes to solve the classic combinatorial optimization problem known as traveling salesman problem. A recurrent neural network was used in the system of optimization to search the shorter path. The structural topology linking the feedbacks of the network adopted here is known by Wang recurrent network. As learning rule to find the appropriate values of the weights was used the simultaneous perturbation with stochastic approximation. A detailed bibliographical revision on multivariable optimization with simultaneous perturbation is also described. Comparative results with other different techniques applied to the traveling salesman are still presented for validation purposes.

This diploma thesis is devided into theoretic and practice parts. Theoretic part contains basic information about history and development of Artificial Neural Networks (ANN) from last century till present. Prove of the theoretic section is discussed in the practice part, for example learning, training each types of topology of artificial neural networks on some specifics works. Simulation of this networks and then describing results. Aim of thesis is simulation of the active networks element controlling by artificial neural networks. It means learning, training and simulation of designed neural network. This section contains algorithm of ports switching by address with Hopfield's networks, which used solution of typical Trade Salesman Problem (TSP). Next point is to sketch problems with optimalization and their solutions. Hopfield's topology is compared with Recurrent topology of neural networks (Elman's and Layer Recurrent's topology) their main differents, their advantages and disadvantages and supposed their solution of optimalization in controlling of network's switch. From thesis experience is introduced solution with controll function of ANN in active networks elements in the future.

Developing and improving upon a good empirical model for an engine can be time-consuming and costly. The goal of this thesis has been to evaluate data-driven modelling, specifically neural networks, to see how well it can handle training for some static models like the mass flow of air into the cylinder, mean effective pressure and pump mean effective pressure but also for transient modelling, specifically the exhaust gas temperature. These models are evaluated against the classical empirical models to see if neural networks are a viable modelling option. This is done with five different types of neural networks which are trained. These are the feed-forward neural network, Nonlinear autoregressive exogenous model network, layer recurrent network, long short term memory network and gated recurrent network.The inputs were determined by looking at more simple physical models but also looking at the covariance to determine the usefulness of the input. If the calculation time is small for the specific network, the neural network structure is tested and optimized by training many networks and finding the median/mean result for that specific test.The result has shown that the static models are handled very well by the most simple feed-forward network. For the exhaust temperature, both NARX and Layer recurrent network could predict and handle it well giving results very close to the empirical models and could be a viable option for transient modelling, on the other hand, Long short term memory, gated recurrent network and the feed-forward network had trouble predicting the exhaust gas temperature and returned bad results while training.

Human activity data is typically represented as time series data, and RNNs, often with LSTM cells, are commonly used for recognition in this field. However, RNNs and LSTM-RNNs are often too resource-intensive for real-time applications on resource constrained devices, making them unsuitable. This thesis project is carried out at Wrlds AB, Stockholm. At Wrlds, all machine learning is run in the cloud, but they have been attempting to run their AI algorithms on their embedded devices. The main task of this project was to investigate alternative network structures to minimize the size of the networks to be used on human activity data. This thesis investigates the use of Fast GRNN, a deep learning algorithm developed by Microsoft researchers, to classify human activity on resource-constrained devices. The FastGRNN algorithm was compared to state-of-the-art RNNs, LSTM, GRU, and Simple RNN in terms of accuracy, classification time, memory usage, and energy consumption. This research is limited to implementing the FastRNN algorithm on Nordic SoCs using their SDK and TensorFlow Lite Micro. The result of this thesis shows that the proposed network has similar performance as LSTM networks in terms of accuracy while being both considerably smaller and faster, making it a promising solution for human activity recognition on embedded devices with limited computational resources and merits further investigation. / Rörelse igenkännings analys är oftast representerat av tidsseriedata där ett RNN modell meden LSTM arkitektur är oftast den självklara vägen att ta. Dock så är denna arkitektur väldigt resurskrävande för applikationer i realtid och gör att det uppstår problem med resursbegränsad hårdvara. Detta examensarbete är utfört i samarbete med Wrlds Technologies AB. På Wrlds så körs deras maskin inlärningsmodeller på molnet och lokalt på mobiltelefoner. Wrlds har nu påbörjat en resa för att kunna köra modeller direkt på små inbyggda system. Examensarbete kommer att utvärdera en FastGRNN som är en NN-arkitektur utvecklad av Microsoft i syfte att användas på resurs begränsad hårdvara. FastGRNN algoritmen jämfördes med andra högkvalitativa arkitekturer som RNNs, LSTM, GRU och en simpel RNN. Träffsäkerhet, klassifikationstid, minnesanvändning samt energikonsumtion användes för att jämföra dom olika varianterna. Detta arbete kommer bara att utvärdera en FastGRNN algoritm på en Nordic SoCs och kommer att användas deras SDK samt Tensorflow Lite Micro. Resultatet från detta examensarbete visar att det utvärderade nätverket har liknande prestanda som ett LSTM nätverk men också att nätverket är betydligt mindre i storlek och därmed snabbare. Detta betyder att ett FastGRNN visar lovande resultat för användningen av rörelseigenkänning på inbyggda system med begränsad prestanda kapacitet.

With the inception of music streaming and media content delivery platforms, there has been a tremendous increase in the music available on the internet and the metadata associated with it. In this study, we address the problem of violin artist identification, which tries to classify the performing artist based on the learned features. Even though numerous previous works studied the problem in detail and developed features and deep learning models that can be used, an interesting fact was that most studies focused on artist identification in western popular music and less on Indian classical music. For the same reason, there was no standardized dataset for this purpose. Hence, we curated a new dataset consisting of audio recordings from 6 renowned South Indian Carnatic violin artists. In this study, we explore the use of log-Mel-spectrogram feature and the embeddings generated by a pre-learned VGGish network on a Convolutional Neural Network and Convolutional Recurrent Neural Network Model. From the experiments, we observe that the Convolutional Recurrent Neural Network model trained using the log-Mel-spectrogram feature gave the optimal performance with a classification accuracy of 71.70%. / Med starten av plattformar för musikströmning och leverans av mediainnehåll har det skett en enorm ökning av musiken tillgänglig på internet och den metadata som är associerad med den. I denna studie tar vi upp problemet med fiolkonstnärsidentifikation, som försöker klassificera den utövande konstnären utifrån de inlärda dragen. Även om många tidigare verk studerade problemet i detalj och utvecklade funktioner och modeller för djupinlärning som kan användas, var ett intressant faktum att de flesta studier fokuserade på artistidentifiering i västerländsk populärmusik och mindre på indisk klassisk musik. Av samma anledning fanns det ingen standardiserad datauppsättning för detta ändamål. Därför kurerade vi en ny datauppsättning bestående av ljudinspelningar från 6 kända sydindiska karnatiska violinkonstnärer. I den här studien utforskar vi användningen av log-Melspektrogramfunktionen och inbäddningarna som genereras av ett förinlärt VGGishnätverk på ett Convolutional Neural Network och Convolutional Recurrent Neural Network Model. Från experimenten observerar vi att modellen Convolutional Recurrent Neural Network tränad med hjälp av log-Mel-spektrogramfunktionen gav optimal prestanda med en klassificeringsnoggrannhet på 71,70%.

In many types of sports, on-screen graphics such as an reconstructed ball trajectory, can be displayed for spectators or players in order to increase understanding. One sub-problem of trajectory reconstruction is tracking of ball positions, which is a difficult problem due to the fast and often complex ball movement. Historically, physics based techniques have been used to track ball positions, but this thesis investigates using a recurrent neural network design, in the application of tracking bouncing golf balls. The network is trained and tested on synthetically created golf ball shots, created to imitate balls shot out from a golf driving range. It is found that the trained network succeeds in tracking golf balls during bounce and roll, with an error rate of under 11 %. / Grafik visad på en skärm, så som en rekonstruerad bollbana, kan användas i många typer av sporter för att öka en åskådares eller spelares förståelse. För att lyckas rekonstruera bollbanor behöver man först lösa delproblemet att följa en bolls positioner. Följning av bollpositioner är ett svårt problem på grund av den snabba och ofta komplexa bollrörelsen. Tidigare har fysikbaserade tekniker använts för att följa bollpositioner, men i den här uppsatsen undersöks en metod baserad på återkopplande neurala nätverk, för att följa en studsande golfbolls bana. Nätverket tränas och testas på syntetiskt skapade golfslag, där bollbanorna är skapade för att imitera golfslag från en driving range. Efter träning lyckades nätverket följa golfbollar under studs och rull med ett fel på under 11 %.

Recurrent Neural Networks (RNNs) are pivotal in deep learning for time series prediction, but they suffer from 'exploding values' and 'gradient decay,' particularly when learning temporally distant interactions. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) have addressed these issues to an extent, but the precise mitigating mechanisms remain unclear. Moreover, the success of feedforward neural networks in time series tasks using an 'attention mechanism' raises questions about the solutions offered by LSTMs and GRUs. This study explores an alternative explanation for the challenges faced by RNNs in learning long-range correlations in the input data. Could the issue lie in the movement of the representations - how hidden nodes store and process information - across nodes instead of localization? Evidence presented suggests that RNNs can indeed possess "moving representations," with certain training conditions reducing this movement. These findings point towards the necessity of further research on localizing representations.

Achieving appropriate and natural backchanneling behavior in social robots remains a challenge in Human-Robot Interaction (HRI). This thesis addresses this issue by utilizing methods from Safe Reinforcement Learning in particular shielding to improve social robot backchanneling behavior. The aim of the study is to develop and implement a safety shield that guarantees appropriate backchanneling. In order to achieve that, a Recurrent Neural Network (RNN) is trained on a human-human conversational dataset. Two agents are built; one uses a random algorithm to backchannel and another uses shields on top of its algorithm. The two agents are tested using a recorded human audio, and later evaluated in a between-subject user study with 41 participants. The results did not show any statistical significance between the two conditions, for the chosen significance level of α < 0.05. However, we observe that the agent with shield had a better listening behavior, more appropriate backchanneling behavior and missed less backchanneling opportunities than the agent without shields. This could indicate that shields have a positive impact on the robot’s behavior. We discuss potential explanations for why we did not obtain statistical significance and shed light on the potential for further exploration. / Att uppnå lämpligt och naturligt upbbackningsbeteende i sociala robotar är fortfarande en utmaning i Människa-Robot Interaktion (MRI). Den här avhandlingen tar upp detta problem genom att använda metoder från säker förstärkningsinlärning i synnerhet avskärmning för att förbättra sociala robotars upbbackningsbeteende. Syftet med studien är att utveckla och implementera en säkerhetsavskärmning som garanterar lämplig upbbackning. För att uppnå det, tränas ett återkommande neuralt nätverk på en människa-människa konversationsdatamängd. Två agenter byggs; en använder en slumpmässig algoritm för att upbbacka och en annan använder avskärmninng ovanpå sin algoritm. De två agenterna testas med hjälp av ett inspelat mänskligt ljud och utvärderas senare i en användarstudie med 41 deltagare. Resultaten visade inte någon statistisk signifikans mellan de två skicken, för den valda signifikansnivån < 0, 05. Vi observerar dock att agenten med avskärmning hade ett bättre lyssningsbeteende, mer lämplig upbbackningsbeteende och missade mindre upbbacknings-möjligheter än agenten utan avskärmning. Detta kan indikera att avskärmning har en positiv inverkan på robotarnas beteende. Vi diskuterar potentiella förklaringar till varför vi inte fick statistisk signifikans och belyser potentialen för ytterligare utforskning.

In this thesis we examine the question whether it is possible to model approach and avoidance behaviour with probabilistic machine learning. The results from this project will primarily aid in our collective understanding of human existence. Secondly, it will extend the knowledge with regards to probabilistic machine learning in the Neuroscience domain. We aid this through building a Variational Recurrent Neural Network (VRNN) that is trained on Electroencephalography (EEG)-data from participants that is subjected to odours with varying pleasantness. The pleasantness of the odours is used to divide the participants into two classes based on their self reported experience. This data is used to train the VRNN. The performance of the VRNN is evaluated by how well we are able to reconstruct the original data from a low dimensional latent representation. In this task the model performs on a similar level as related works. We further investigate how changes in the latent space effects reconstructed data. Despite being disentangled, the latent variables are hard to interpret. Furthermore we try to classify and cluster the latent space as either approach or avoidance behaviour with a Support Vector Machine and Uniform Manifold Approximation. The classification results are only slightly better than random, indicating that the learned latent space is not suitable for the task This is most likely due to the patterns that make up approach and avoidance behaviour is seen as noise by the VRNN. This leads to the patterns not being accurately modelled. This is shown by the evidence that frontal α -asymmetry that exists in the data is not reconstructed by the model. The conclusion is therefore that a VRNN is less suitable for modelling underlying behaviour from raw EEG data due to the low signal to noise ratio. We instead suggests to focus on specific frequency ranges in specific regions when applying machine learning in this domain. / Den här uppsatsen behandlar frågan huruvida det är möjligt att modellera närmande och frånstötande beteendemönster med hjälp av maskininlärning. Resultaten från detta projekt ämnar huvudsakligen att främja vidare förståelse av den mänskliga existensen. Vidare ämnar den även att utvidga förståelsen av hur probabilistisk maskininlärning kan användas för att utforska dylika hänseenden. Vi genomför detta genom att bygga en Variational Recurrent Neural Network-modell (VRNN) som tränas på data från experiment där personer utsätts för olika lukter samtidigt som deras Elektroencefalografi (EEG) spelas in. Deltagarna delas in i två klasser beroende på deras självrapporterade upplevelse av luktens njutbarhet. Maskininlärningsmodellen utvärderas genom att vi analyserar hur väl den lyckas rekonstruera datan. Detta lyckas den väl med. Vidare så undersöker vi hur förändringar i modellens latenta rum påverkar rekonstrueringen av datan. Resultaten från det experimentet är ej tydliga. Vidare så försöker vi klassificera och klustra det latenta rummet med avseende på närmande och frånstötande beteende med hjälp av en Support Vector Machine och Uniform Manifold Approximation. Resultaten från dessa experiment är att vi inte lyckas klassificera eller klustra det latenta rummet med avseende på närmande och frånstötande beteende bättre än slumpen. Vi argumenterar för att detta beror på att de underliggande mönster som skapar dessa beteenden ses som brus av VRNN-modellen och därmed inte modelleras. Detta visas genom att frontal α-asymmetri som existerar i datan ej rekonstrueras av modellen. Slutsaten blir därmed att en VRNN är mindre passande att använda vid modellering av underliggande beteenden av obehandlad EEG data. Detta på grund av det låga signal till brus-förhållandet i EEG-datan. Vi föreslår att istället fokusera på specifika frekvensområden i specifika hjärnregioner när maskininlärning appliceras på EEG.