John Gallagher (Advisor)
Doctor of Philosophy (PhD)
The field of Evolvable Hardware (EH) has recently gained a lot of interest due to the novel methodology it offers for designing electrical circuits and machines. EH techniques involve configuring a reconfigurable hardware platform with the aid of learning engines such as evolutionary algorithms. The EH devices normally act as closed loop controllers with the capability of learning necessary control laws adaptively. Current EH practices have several shortcomings, which have restricted their use as reliable controllers. This dissertation will present an improved EH device based on behavioral reconfigurability that addresses the current open challenges in the field of analog Evolvable Hardware. This EH device is based on Continuous Time Recurrent Neural Network (CTRNN). The design and implementation of the CTRNN-EH device and a custom designed evolutionary learning engine will be presented in this work. In addition to answering the open challenges in the field of EH, this dissertation will also provide a novel programming circuitry to by which a VLSI CTRNN can be effectively programmed. Furthermore, a closed loop calibration scheme based on Evolutionary Algorithms is presented to address the effects of random offset variations in the CTRNN design.
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
Copyright 2007, all rights reserved. This open access ETD is published by Wright State University and OhioLINK.