Thesis
Feedback control for manipulating network opinion dynamics and adaptive software analysis
Washington State University
Master of Science (MS), Washington State University
08/2020
DOI:
https://doi.org/10.7273/000004167
Handle:
https://hdl.handle.net/2376/125326
Abstract
This thesis is concerned with control of a network opinion dynamics by selfish agents. A network opinion dynamics with linear averaging updates and a selfish agent is considered for this study. The selfish agent uses a projection (statistic) of network opinions in feedback to regulate the dynamics, with the goal of driving other agents' opinions to a desired reference goal. The selfish agent in this work is modeled as proportional and proportional-integral feedback controller to manipulate network opinions of local agents or global network agents. Further the model is extended to consider nonlinear capping conditions, where all agents opinions are bounded between 0 and 1. Finally, simulations of the manipulated network dynamics under variety of scenarios are developed. The empirical analyses and simulations were conducted using a real-world social network, representing frequent interactions between dolphins in the Doubtful Sound, a remote Fjord in New Zealand. Additionally the thesis presents an application of feedback control in designing a self-adapting software analysis system. The results presented in this thesis motivates for use of distributed control for software analysis systems.
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Details
- Title
- Feedback control for manipulating network opinion dynamics and adaptive software analysis
- Creators
- Chandan Dhal
- Contributors
- Sandip Roy (Advisor) - Washington State University, Electrical Engineering and Computer Science, School of
- Awarding Institution
- Washington State University
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University
- Identifiers
- 99900890769601842
- Language
- English
- Resource Type
- Thesis