An Analog Evolvable Hardware Device for Active Control

 
 
 
 

An Analog Evolvable Hardware Device for Active Control

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dc.contributor.advisor Gallagher, John C. en_US
dc.contributor.author Vigraham, Saranyan A. en_US
dc.date.accessioned 2008-07-11T14:49:30Z
dc.date.available 2008-07-11T14:49:30Z
dc.date.created 2007 en_US
dc.date.issued 2008-07-11T14:49:30Z
dc.identifier.uri http://rave.ohiolink.edu/etdc/view?acc_num=wright1195506953 en_US
dc.identifier.uri http://hdl.handle.net/2374.OX/19521
dc.description 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. en_US
dc.format application/pdf en_US
dc.format 170p. en_US
dc.rights unrestricted en_US
dc.rights Copyright and permissions information available at the source archive en_US
dc.subject Analog VLSI en_US
dc.subject Evolvable Hardware en_US
dc.subject Continuous Time Recurrent Neural Network en_US
dc.subject Evolutionary Algorithms en_US
dc.subject Neural Networks in hardware en_US
dc.title An Analog Evolvable Hardware Device for Active Control en_US
dc.type Electronic Thesis or Dissertation en_US
dc.degree.name PhD en_US
dc.degree.level doctoral en_US
dc.degree.discipline Computer Science and Engineering PhD en_US
dc.degree.grantor Wright State University en_US
dc.contributor.publisher Wright State University / OhioLINK en_US

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