Bachelor and Master Theses

Title: Real-time Process Modelling Based on Big Data Stream Learning
Level: Advanced
Description: Most control systems now are assumed to be unchangeable, but this is an ideal situation. In real applications, they are often accompanied with many changes. Some of changes are from environment changes, and some are system requirements. So, it is necessary to model a real-time control system process. In this way, control system model can adjust itself and give a suggestion for next input, which indicates the accuracy of states under control highly depends on quality of the process model.
In this thesis, we choose recurrent neural network to model process because it is a kind of cheap and fast artificial intelligence. It does not need any big database to support and search, and only needs simple calculation instead. All small units called neuron are linear combination, but a neural network made up of neurons can perform some complex and non-linear functionalities. For training part, Backpropagation and Kalman filter are used together. Backpropagation is a widely-used and stable optimization algorithm. Kalman filter is new to gradient-based optimization, but it has been proved to converge faster than other traditional first-order-gradient-based algorithms.
Several experiments were prepared to compare new and existent algorithms under various circumstances. The first set of experiments are static systems and they are only used to investigate convergence rate and accuracy of different algorithms. The second set of experiments are time-varying systems and the purpose is to take one more attribute, adaptivity, into consideration.
Prel. end date: 2017-05-17
Presentation date: 2017-06-12
Student: Fan He
IDT supervisor: Ning Xiong, +46-21-151716
Examinator: Thomas Nolte
Thomas Nolte, +46-21-103178

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2017-06-29, 16:03

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