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open access

Computational Complexity of Hopfield Networks

Description: There are three main results in this dissertation. They are PLS-completeness of discrete Hopfield network convergence with eight different restrictions, (degree 3, bipartite and degree 3, 8-neighbor mesh, dual of the knight's graph, hypercube, butterfly, cube-connected cycles and shuffle-exchange), exponential convergence behavior of discrete Hopfield network, and simulation of Turing machines by discrete Hopfield Network.
Date: August 1998
Creator: Tseng, Hung-Li
Partner: UNT Libraries
open access

A Multi-Time Scale Learning Mechanism for Neuromimic Processing

Description: Learning and representing and reasoning about temporal relations, particularly causal relations, is a deep problem in artificial intelligence (AI). Learning such representations in the real world is complicated by the fact that phenomena are subject to multiple time scale influences and may operate with a strange attractor dynamic. This dissertation proposes a new computational learning mechanism, the adaptrode, which, used in a neuromimic processing architecture may help to solve some of these… more
Date: August 1994
Creator: Mobus, George E. (George Edward)
Partner: UNT Libraries
open access

An Evaluation of Backpropagation Neural Network Modeling as an Alternative Methodology for Criterion Validation of Employee Selection Testing

Description: Employee selection research identifies and makes use of associations between individual differences, such as those measured by psychological testing, and individual differences in job performance. Artificial neural networks are computer simulations of biological nerve systems that can be used to model unspecified relationships between sets of numbers. Thirty-five neural networks were trained to estimate normalized annual revenue produced by telephone sales agents based on personality and biogra… more
Date: August 1995
Creator: Scarborough, David J. (David James)
Partner: UNT Libraries
open access

Classification by Neural Network and Statistical Models in Tandem: Does Integration Enhance Performance?

Description: The major purposes of the current research are twofold. The first purpose is to present a composite approach to the general classification problem by using outputs from various parametric statistical procedures and neural networks. The second purpose is to compare several parametric and neural network models on a transportation planning related classification problem and five simulated classification problems.
Date: December 1998
Creator: Mitchell, David
Partner: UNT Libraries
open access

Temporal Connectionist Expert Systems Using a Temporal Backpropagation Algorithm

Description: Representing time has been considered a general problem for artificial intelligence research for many years. More recently, the question of representing time has become increasingly important in representing human decision making process through connectionist expert systems. Because most human behaviors unfold over time, any attempt to represent expert performance, without considering its temporal nature, can often lead to incorrect results. A temporal feedforward neural network model that can … more
Date: December 1993
Creator: Civelek, Ferda N. (Ferda Nur)
Partner: UNT Libraries
open access

A Heuristic Procedure for Specifying Parameters in Neural Network Models for Shewhart X-bar Control Chart Applications

Description: This study develops a heuristic procedure for specifying parameters for a neural network configuration (learning rate, momentum, and the number of neurons in a single hidden layer) in Shewhart X-bar control chart applications. Also, this study examines the replicability of the neural network solution when the neural network is retrained several times with different initial weights.
Date: December 1993
Creator: Nam, Kyungdoo T.
Partner: UNT Libraries
open access

Toward Supporting Fine-Grained, Structured, Meaningful and Engaging Feedback in Educational Applications

Description: Recent advancements in machine learning have started to put their mark on educational technology. Technology is evolving fast and, as people adopt it, schools and universities must also keep up (nearly 70% of primary and secondary schools in the UK are now using tablets for various purposes). As these numbers are likely going to follow the same increasing trend, it is imperative for schools to adapt and benefit from the advantages offered by technology: real-time processing of data, availabilit… more
Date: December 2018
Creator: Bulgarov, Florin Adrian
Partner: UNT Libraries
open access

On-Loom Fabric Defect Inspection Using Contact Image Sensors and Activation Layer Embedded Convolutional Neural Network

Description: Malfunctions on loom machines are the main causes of faulty fabric production. An on-loom fabric inspection system is a real-time monitoring device that enables immediate defect detection for human intervention. This dissertation presented a solution for the on-loom fabric defect inspection, including the new hardware design—the configurable contact image sensor (CIS) module—for on-loom fabric scanning and the defect detection algorithms. The main contributions of this work include (1) creating… more
Date: December 2018
Creator: Ouyang, Wenbin
Partner: UNT Libraries
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