DOI

10.17077/etd.d47y-9s7b

Document Type

Dissertation

Date of Degree

Summer 2019

Degree Name

PhD (Doctor of Philosophy)

Degree In

Industrial Engineering

First Advisor

Lendasse, Amaury

First Committee Member

Farag, Amany

Second Committee Member

Chen, Yong

Third Committee Member

McGehee, Daniel

Fourth Committee Member

Ratner, Edward

Fifth Committee Member

Lendasse, Amaury

Abstract

This dissertation explores Random Neural Networks (RNNs) in several aspects and their applications. First, Novel RNNs have been proposed for dimensionality reduction and visualization. Based on Extreme Learning Machines (ELMs) and Self-Organizing Maps (SOMs) a new method is created to identify the important variables and visualize the data. This technique reduces the curse of dimensionality and improves furthermore the interpretability of the visualization and is tested on real nursing survey datasets. ELM-SOM+ is an autoencoder created to preserves the intrinsic quality of SOM and also brings continuity to the projection using two ELMs. This new methodology shows considerable improvement over SOM on real datasets. Second, as a Supervised Learning method, ELMs has been applied to the hierarchical multiscale method to bridge the the molecular dynamics to continua. The method is tested on simulation data and proven to be efficient for passing the information from one scale to another. Lastly, the regularization of ELMs has been studied and a new regularization algorithm for ELMs is created using a modified Lanczos Algorithm. The Lanczos ELM on average divide computational time by 20 and reduce the Normalized MSE by 14% comparing with regular ELMs.

Keywords

Data Visualization, Dimensionality Reduction, Feature Selection, Lanczos Algorithm, Random Neural Networks, Regularization

Pages

xii, 148 pages

Bibliography

Includes bibliographical references (pages 134-148).

Copyright

Copyright © 2019 Renjie Hu

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