Zriadenie Fakulty informatiky a informačných technológií - FIIT STU

Annual report 2009 45Kalman filter and TD(l) approach. Our results show that extendedKalman filter is able to create a game strategy after playing a considerablyfewer number of games.Student name: Matej MakulaDegree program: Applied InformaticsThesis title: Benefits and Constrains of Recurrent Neural Networks for ProcessingSymbolic SequencesSupervisor: Ľubica Beňušková, Associate ProfessorDefended on: July 8, 2009Annotation: This thesis studies properties of recurrent neural networks whileprocessing symbolic inputs. We focused mainly on their relation anddescription of their behaviour in the terms of dynamical systems. Wedescribe the dynamics of randomly initialized neural network and itsrelation to Markov prediction models of variable length. In the mainpart of our work we present usability of methods for visualization,clusterization and the state space analysis as an effective tool forthorough study of recurrent networks capabilities on prediction tasks.In experimental part we focus on studying changes occurring duringtraining. We are mostly interested in the change of naïve Markoviandynamics of randomly initialized network during training in relationto various factors such as: input sequence, training algorithm, networkarchitecture, number of hidden units, etc. We focused not onlyon simple recurrent network before and after training, but also on thecomputational capabilities of the new approach called echo state networks.It uses large randomly initialized neural reservoir, which dynamicsis the subject of our interest. We demonstrate benefits andconstraints of this approach based on the results of our experimentsand differences identified after recurrent networks training.Student name: Peter TrebatickýDegree program: Artificial IntelligenceThesis title: Prediction of Dynamical Systems by Recurrent Neural NetworksSupervisor: Jirí Pospíchal, ProfessorDefended on: June 25, 2009Annotation: Recurrent neural networks in general achieve better results in predictionof time series then feedforward networks. Echo state neural networksseem to be one alternative to them. I have shown on the task oftext correction, that they achieve slightly better results compared toalready known method based on Markovov model. The major part ofthis work is focused on alternatives to recurrent neural networkstraining that are based on Kalman filtration modifications. I describein detail the training by filters: Extended Kalman Filter, UnscentedKalman Filter (UKF), nprKF Filter and their joint versions UKFj andnprKFj. Filter UKFj in context of recurrent neural networks wasprobably firstly described in my work. Contribution of this work ispresentation of simpler equations for individual filters, because theyare modified specifically for recurrent neural network training. I