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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 problems. The adaptrode is shown to emulate the dynamics of real biological synapses and represents a significant departure from the classical weighted input scheme of conventional artificial neural networks. Indeed the adaptrode is shown, by analysis of the deep structure of real synapses, to have a strong structural correspondence with the latter in terms of multi-time scale biophysical processes. Simulations of an adaptrode-based neuron and a small network of neurons are shown to have the same learning capabilities as invertebrate animals in classical conditioning. Classical conditioning is considered a fundamental learning task in animals. Furthermore, it is subject to temporal ordering constraints that fulfill the criteria of causal relations in natural systems. It may offer clues to the learning of causal relations and mechanisms for causal reasoning. The adaptrode is shown to solve an advanced problem in classical conditioning that addresses the problem of real world dynamics. A network is able to learn multiple, contrary associations that separate in time domains, that is a long-term memory can co-exist with a short-term contrary memory without destroying the former. This solves the problem of how to deal with meaningful transients while maintaining long-term memories. Possible applications of adaptrode-based neural networks are explored and suggestions for future research are made.
Date: August 1994
Creator: Mobus, George E. (George Edward)
Partner: UNT Libraries

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 be applied to a number of neural network application areas, including connectionist expert systems, has been introduced. The neural network model has a multi-layer structure, i.e. the number of layers is not limited. Also, the model has the flexibility of defining output nodes in any layer. This is especially important for connectionist expert system applications. A temporal backpropagation algorithm which supports the model has been developed. The model along with the temporal backpropagation algorithm makes it extremely practical to define any artificial neural network application. Also, an approach that can be followed to decrease the memory space used by weight matrix has been introduced. The algorithm was tested using a medical connectionist expert system to show how best we describe not only the disease but also the entire course of the disease. The system, first, was trained using a pattern that was encoded from the expert system knowledge base rules. Following then, series of experiments were carried out using the temporal model and the temporal backpropagation algorithm. The first series of experiments was done to determine if the training process worked as predicted. In the second series of experiments, the weight matrix in the trained system was defined as a function of time intervals before presenting the system with the learned patterns. The result of the two experiments indicate that both approaches produce correct results. The only difference between the two results ...
Date: December 1993
Creator: Civelek, Ferda N. (Ferda Nur)
Partner: UNT Libraries

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

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 biographic predictors using concurrent validation data (N=1085). Accuracy of the neural estimates was compared to OLS regression and a proprietary nonlinear model used by the participating company to select agents.
Date: August 1995
Creator: Scarborough, David J. (David James)
Partner: UNT Libraries

Freeform Cursive Handwriting Recognition Using a Clustered Neural Network

Description: Optical character recognition (OCR) software has advanced greatly in recent years. Machine-printed text can be scanned and converted to searchable text with word accuracy rates around 98%. Reasonably neat hand-printed text can be recognized with about 85% word accuracy. However, cursive handwriting still remains a challenge, with state-of-the-art performance still around 75%. Algorithms based on hidden Markov models have been only moderately successful, while recurrent neural networks have delivered the best results to date. This thesis explored the feasibility of using a special type of feedforward neural network to convert freeform cursive handwriting to searchable text. The hidden nodes in this network were grouped into clusters, with each cluster being trained to recognize a unique character bigram. The network was trained on writing samples that were pre-segmented and annotated. Post-processing was facilitated in part by using the network to identify overlapping bigrams that were then linked together to form words and sentences. With dictionary assisted post-processing, the network achieved word accuracy of 66.5% on a small, proprietary corpus. The contributions in this thesis are threefold: 1) the novel clustered architecture of the feed-forward neural network, 2) the development of an expanded set of observers combining image masks, modifiers, and feature characterizations, and 3) the use of overlapping bigrams as the textual working unit to assist in context analysis and reconstruction.
Date: August 2015
Creator: Bristow, Kelly H.
Partner: UNT Libraries

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

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

Hopfield Networks as an Error Correcting Technique for Speech Recognition

Description: I experimented with Hopfield networks in the context of a voice-based, query-answering system. Hopfield networks are used to store and retrieve patterns. I used this technique to store queries represented as natural language sentences and I evaluated the accuracy of the technique for error correction in a spoken question-answering dialog between a computer and a user. I show that the use of an auto-associative Hopfield network helps make the speech recognition system more fault tolerant. I also looked at the available encoding schemes to convert a natural language sentence into a pattern of zeroes and ones that can be stored in the Hopfield network reliably, and I suggest scalable data representations which allow storing a large number of queries.
Access: This item is restricted to the UNT Community Members at a UNT Libraries Location.
Date: May 2004
Creator: Bireddy, Chakradhar
Partner: UNT Libraries

A Neural Network Configuration Compiler Based on the Adaptrode Neuronal Model

Description: A useful compiler has been designed that takes a high level neural network specification and constructs a low level configuration file explicitly specifying all network parameters and connections. The neural network model for which this compiler was designed is the adaptrode neuronal model, and the configuration file created can be used by the Adnet simulation engine to perform network experiments. The specification language is very flexible and provides a general framework from which almost any network wiring configuration may be created. While the compiler was created for the specialized adaptrode model, the wiring specification algorithms could also be used to specify the connections in other types of networks.
Date: December 1992
Creator: McMichael, Lonny D. (Lonny Dean)
Partner: UNT Libraries