First Advisor

Christof Teuscher

Date of Publication

1-1-2011

Document Type

Thesis

Degree Name

Master of Science (M.S.) in Computer Science

Department

Computer Science

Language

English

Subjects

Boolean networks, Random automata networks, Adaptation and criticality, Complex adaptive systems, Learning and generalization, Network information processing, Machine learning, Neural networks (Computer science), Adaptive control systems

DOI

10.15760/etd.193

Physical Description

1 online resource (viii, 103 p.)

Abstract

We extend the study of learning and generalization in feed forward Boolean networks to random Boolean networks (RBNs). We explore the relationship between the learning capability and the network topology, the system size, the training sample size, and the complexity of the computational tasks. We show experimentally that there exists a critical connectivity Kc that improves the generalization and adaptation in networks. In addition, we show that in finite size networks, the critical K is a power-law function of the system size N and the fraction of inputs used during the training. We explain why adaptation improves at this critical connectivity by showing that the network ensemble manifests maximal topological diversity near Kc. Our work is partly motivated by self-assembled molecular and nanoscale electronics. Our findings allow to determine an automata network topology class for efficient and robust information processing.

Rights

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

Comments

Portland State University. Dept. of Computer Science

Persistent Identifier

http://archives.pdx.edu/ds/psu/7071

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