Distributed Adaptation Over Networks with Applications to Biological Networks
May 01, 2013
from 09:00 AM to 12:00 PM
|Where||Engr. IV Bldg. Faraday Rm. 67-124|
|Contact Name||Sheng-Yuan Tu|
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Advisor: Ali H. Sayed
Adaptive networks consist of a collection of nodes with adaptation and learning abilities. The nodes interact with each other on a local level and diffuse information across the network to solve estimation and inference tasks in real-time. In this dissertation, we first examine and compare the mean-square performance of two main strategies for distributed estimation over networks: consensus strategies and diffusion strategies. The analysis confirms that diffusion networks converge faster and reach lower mean-square deviation than consensus networks, and that their mean-square stability is insensitive to the choice of the combination weights. We then incorporate node mobility into the design of the networks and demonstrate that the resulting strategies are well suited to model various types of self-organized behavior observed in biological networks.
We also examine the effect of heterogeneous sources of information on network performance. In one scenario, we consider two types of agents: informed and uninformed. Informed agents receive new data regularly and perform consultation and in-network processing tasks, while uninformed agents participate solely in the consultation tasks. It is established that if the set of informed agents is enlarged, the convergence rate of the network becomes faster albeit at the possible expense of some deterioration in mean-square performance. In a second scenario, we study the situation in which the data observed by the agents may arise from two different distributions or models. We develop and study a procedure by which the entire network can be made to follow one objective or the other through a distributed and collaborative decision process. The results are useful to model situations where the agents in biological networks need to decide between multiple options, such as deciding between moving towards one food source.
The results in this dissertation reveal some interesting phenomena that relate to adaptation over networks: more information is not necessarily better and the way by which information is processed and propagated through the network matters: small variations can lead to catastrophic failures. The dissertation also reveals the convenience of using diffusion strategies to model sophisticated behavior exhibited by biological networks such as fish schooling and prey-predator behavior.
Sheng-Yuan Tu received the B.S. and MS degrees in electrical engineering from the National Taiwan University (NTU), Taiwan, in 2005 and 2007, respectively. From 2008 to 2009, he was a Research Assistant in the Wireless Broadband Communication System Laboratory at NTU. He is currently pursuing the Ph.D. degree in the department of electrical engineering, University of California, Los Angeles (UCLA). He is a member of the Adaptive Systems Laboratory at UCLA. His research interests are in adaptive filtering, optimization, and self-organization in biological systems. His current research activities are focused on distributed estimation and collective behavior in biological systems.