Vibrational control of chaos in artificial neural networks

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dc.contributor.advisor Gaborski, Roger Bean, Ralph 2009-10-19T14:00:41Z 2009-10-19T14:00:41Z 2009
dc.description.abstract Neural networks with chaotic baseline behavior are interesting for their experimental bases in both biological relevancy and engineering applicability. In the engineering case, the literature still lacks a robust study of the interrelationship between particular chaotic baseline network dynamics and 'online' or 'driving' inputs. We ask the question, for a particular neural network with chaotic baseline behavior, what periodic inputs of minimal magnitude have a stabilizing effect on network dynamics? A genetic algorithm is developed for the task. A systematic comparison of different genetic operators is carried out where each operator-combination is ranked by the optimality of solutions found. The algorithm reaches acceptable results and _finds input sequences with largest elements on the order of 10^3. Lastly, an illustration of the complexity of the fitness space is produced by brute-force sampling period-2 inputs and plotting a fitness map of their stabilizing effect on the network.
dc.language.iso en_US
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dc.subject Chaos en_US
dc.subject Control en_US
dc.subject Evolutionary programming en_US
dc.subject Genetic algorithms en_US
dc.subject Lyapunov en_US
dc.subject Neural networks en_US
dc.subject.lcc QA76.87 .B43 2009
dc.subject.lcsh Chaotic behavior in systemsNeural networks (Computer science)
dc.subject.lcsh Genetic algorithms
dc.title Vibrational control of chaos in artificial neural networks
dc.type Thesis College of Computer Science
dc.description.department Department of Computer Science Rochester Institute of Technology

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