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.

Library of Congress Subject Headings

Chaotic behavior in systemsNeural networks (Computer science); Genetic algorithms

Publication Date

2009

Document Type

Thesis

Student Type

- Please Select One -

Department, Program, or Center

Computer Science (GCCIS)

Advisor

Roger Gaborski

Advisor/Committee Member

Peter Anderson

Advisor/Committee Member

Thomas Borrelli

Comments

Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works. Physical copy available through RIT's The Wallace Library at: QA76.87 .B43 2009

Campus

RIT – Main Campus

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