Engineering Commons^™

Deep Learning For Robust Adaptive Inverse Control Of Nonlinear Dynamic Systems: Improved Settling Time With An Autoencoder, Nuha A. S. Alwan, Zahir M. Hussain

Research outputs 2022 to 2026

An adaptive deep neural network is used in an inverse system identification setting to approximate the inverse of a nonlinear plant with the aim of constituting the plant controller by copying to the latter the weights and architecture of the converging deep neural network. This deep learning (DL) approach to the adaptive inverse control (AIC) problem is shown to outperform the adaptive filtering techniques and algorithms normally used in adaptive control, especially when in nonlinear plants. The deeper the controller, the better the inverse function approximation, provided that the nonlinear plant has an inverse and that this inverse can be …

Deep Learning Inspired Feature Engineering For Classifying Tremor Severity, Ahmed Al Taee, Seyedehmarzieh Hosseini, Rami N. Khushaba, Tanveer Zia, Chin-Teng Lin, Adel Al-Jumaily

Research outputs 2022 to 2026

Bio-signals pattern recognition systems can be impacted by several factors with a potential to limit their associated performance and clinical translation. Among these factors, selecting the optimum feature extraction method, that can effectively exploit the interaction between the temporal and spatial information, is the most prominent. Despite the potential of deep learning (DL) models for extracting temporal, spatial, or temporal-spatial information, they are typically restricted by their need for a large amount of training data. The deep wavelet scattering transform (WST) is a relatively recent advancement within the DL literature to replace expensive convolution neural networks models with computationally less …

A Privacy Preserving Online Learning Framework For Medical Diagnosis Applications, Trang Pham Ngoc Nguyen

Theses: Doctorates and Masters

Electronic Health records are an important part of a digital healthcare system. Due to their significance, electronic health records have become a major target for hackers, and hospitals/clinics prefer to keep the records at local sites protected by adequate security measures. This introduces challenges in sharing health records. Sharing health records however, is critical in building an accurate online diagnosis framework. Most local sites have small data sets, and machine learning models developed locally based on small data sets, do not have knowledge about other data sets and learning models used at other sites.

The work in this thesis utilizes …