رسالة جامعية
Modeling and Control of Dynamical Systems with Reservoir Computing
| العنوان: | Modeling and Control of Dynamical Systems with Reservoir Computing |
|---|---|
| المؤلفون: | Canaday, Daniel M. |
| بيانات النشر: | The Ohio State University / OhioLINK, 2019. |
| سنة النشر: | 2019 |
| المجموعة: | Ohiolink ETDs |
| Original Material: | http://rave.ohiolink.edu/etdc/view?acc_num=osu157469471458874Test |
| مصطلحات موضوعية: | Physics, dynamical systems, reservoir computing, recurrent neural networks, artificial neural networks, control engineering, time-series prediction |
| الوصف: | There is currently great interest in applying artificial neural networks to a host of commercial and industrial tasks. Such networks with a layered, feedforward structure are currently deployed in technologies ranging from facial recognition software to self-driving cars. They are favored by a large portion of machine learning experts for a number of reasons. Namely: they possess a documented ability to generalize to unseen data and handle large data sets; there exists a number of well-understood training algorithms and integrated software packages for implementing them; and they have rigorously proven expressive power making them capable of approximating any bounded, static map arbitrarily well. Within the last couple of decades, reservoir computing has emerged as a method for training a different type of artificial neural network known as a recurrent neural network. Unlike layered, feedforward neural networks, recurrent neural networks are non-trivial dynamical systems that exhibit time-dependence and dynamical memory. In addition to being more biologically plausible, they more naturally handle time-dependent tasks such as predicting the load on an electrical grid or efficiently controlling a complicated industrial process. Fully-trained recurrent neural networks have high expressive power and are capable of emulating broad classes of dynamical systems. However, despite many recent insights, reservoir computing remains relatively young as a field. It remains unclear what fundamental properties yield a well-performing reservoir computer. In practice, this results in their design being left to domain experts, despite the actual training process being remarkably simple to implement. In this thesis, I describe a number of numerical and experimental results that expand the understanding and application of reservoir computing techniques. I develop an algorithm for controlling unknown dynamical systems with layers of reservoir computers. I demonstrate this algorithm by stabilizing a range of complex behavior in simulated Lorenz and Mackey-Glass systems. I additionally control an experimental, chaotic circuit with fast fluctuations. Using my technique, I demonstrate control within the measured noise level for some trajectories. This control algorithm is executed on a lightweight, readily-available platform with a 1 MHz closed-loop controller.I also develop a reservoir computing scheme with autonomous, Boolean networks capable of processing complex, real-valued data. I show that this system is capable of emulating, in real time, a benchmark chaotic time-series with high precision and a record-breaking speed of 160 million predictions per second.Finally, I present a technique for obtaining efficient, low dimensional reservoir computers. I demonstrate with numerical examples that the efficient reservoir computers can predict a benchmark time-series more accurately than standard reservoir computers 25 times larger. Through a linear analysis, I find that these efficient reservoirs prefer specific topologies over the random, unstructured reservoir computers that are currently standard. |
| Original Identifier: | oai:etd.ohiolink.edu:osu157469471458874 |
| نوع الوثيقة: | Text |
| اللغة: | English |
| الإتاحة: | http://rave.ohiolink.edu/etdc/view?acc_num=osu157469471458874Test |
| حقوق: | unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
| رقم الانضمام: | edsndl.OhioLink.oai.etd.ohiolink.edu.osu157469471458874 |
| قاعدة البيانات: | Networked Digital Library of Theses & Dissertations |
| الوصف غير متاح. |