National Repository of Grey Literature 12 records found  previous11 - 12  jump to record: Search took 0.01 seconds. 
Maximizing Computational Power by Neuroevolution
Matzner, Filip ; Mráz, František (advisor) ; Pilát, Martin (referee)
Echo state networks represent a special type of recurrent neural networks. Recent papers stated that the echo state networks maximize their computational performance on the transition between order and chaos, the so-called edge of chaos. This work confirms this statement in a comprehensive set of experiments. Afterwards, the best performing echo state network is compared to a network evolved via neuroevolution. The evolved network outperforms the best echo state network, however, the evolution consumes significant computational resources. By combining the best of both worlds, the simplicity of echo state networks and the performance of evolved networks, a new model called locally connected echo state networks is proposed. The results of this thesis may have an impact on future designs of echo state networks and efficiency of their implementation. Furthermore, the findings may improve the understanding of biological brain tissue. 1
Tracking of 3D Movement
Matzner, Filip ; Barták, Roman (advisor) ; Obdržálek, David (referee)
In this thesis, we propose and evaluate a method for tracking short-term move- ment and orientation of a device using only its on-board sensors - accelerometer, gyroscope and magnetometer. A straightforward method of motion tracking is described from the theoretical perspective and afterwards transformed into a practical algorithm. To improve its performance, we enhance the method with a stabilization system, which corrects the bias caused by sensor inaccuracies every time the device stands still. The effectiveness of the proposed method and the merits of the enhancement are evaluated in several experiments with two mo- bile devices. Furthermore, a complete software solution is included, which allows experimentation with smartphone sensors in a user friendly interface. 1

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