Theses and Dissertations at Montana State University (MSU)
Permanent URI for this communityhttps://scholarworks.montana.edu/handle/1/732
Browse
2 results
Search Results
Item Hierarchical fuzzy spectral clustering in campaign finance social networks(Montana State University - Bozeman, College of Engineering, 2021) Wahl, Scott Allen; Chairperson, Graduate Committee: John SheppardCommunity detection in networks is an important tool in understanding complex systems. Finding these communities in complex real-world systems is important in many disciplines, such as computer science, sociology, biology, and others. In this research, we develop an algorithm for performing hierarchical fuzzy spectral clustering. The clustering algorithm is applied to small benchmark problems, as well as a large real-world campaign finance network. Afterwards, we extend the hierarchical fuzzy spectral clustering for use in evolving networks. The discovered communities are tracked through the evolving network and their underlying properties analyzed. Third, we apply association rule mining on community-based partitions of the data. A comparison of the results within and between communities show the effectiveness of this method for adding interpretability to the underlying system. Fourth, we examine the ability of hierarchical fuzzy spectral clustering on a graph to predict behavior that is not present in the graph itself. The results are shown to be effective in predicting votes in the United States legislature based on the campaign finance networks. Finally, we develop an orthogonal spectral autoencoder that is used to perform graph embedding. This approximation model avoids the eigenvector decomposition of the full network, as well as allows out-of-sample spectral clustering. The results show the embedding performs comparably to the full spectral clustering.Item Fuzzy Bayesian networks for prognostics and health management(Montana State University - Bozeman, College of Engineering, 2013) Ryhajlo, Nicholas Frank; Chairperson, Graduate Committee: John SheppardIn systems diagnostics it is often difficult to define test requirements and acceptance thresholds for these tests. A technique that can be used to alleviate this problem is to use fuzzy membership values to represent the degree of membership of a particular test outcome. Bayesian networks are commonly used tools for diagnostics and prognostics; however, they do not accept inputs of fuzzy values. To remedy this we present a novel application of fuzzy Bayesian networks in the context of prognostics and health management. These fuzzy Bayesian networks can use fuzzy values as evidence and can produce fuzzy membership values for diagnoses that can be used to represent component level degradation within a system. We developed a novel execution ordering algorithm used in evaluating the fuzzy Bayesian networks, as well as a method for integrating fuzzy evidence with inferred fuzzy state information. We use three different diagnostic networks to illustrate the feasibility of fuzzy Bayesian networks in the context of prognostics. We are able to use this technique to determine battery capacity degradation as well as component degradation in two test circuits.