Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Computer Engineering
Falconet: Force-Feedback Approach For Learning From Coaching And Observation Using Natural And Experiential Training, Gary Stein
Electronic Theses and Dissertations
Building an intelligent agent model from scratch is a difficult task. Thus, it would be preferable to have an automated process perform this task. There have been many manual and automatic techniques, however, each of these has various issues with obtaining, organizing, or making use of the data. Additionally, it can be difficult to get perfect data or, once the data is obtained, impractical to get a human subject to explain why some action was performed. Because of these problems, machine learning from observation emerged to produce agent models based on observational data. Learning from observation uses unobtrusive and purely …
An Adaptive Multiobjective Evolutionary Approach To Optimize Artmap Neural Networks, Assem Kaylani
An Adaptive Multiobjective Evolutionary Approach To Optimize Artmap Neural Networks, Assem Kaylani
Electronic Theses and Dissertations
This dissertation deals with the evolutionary optimization of ART neural network architectures. ART (adaptive resonance theory) was introduced by a Grossberg in 1976. In the last 20 years (1987-2007) a number of ART neural network architectures were introduced into the literature (Fuzzy ARTMAP (1992), Gaussian ARTMAP (1996 and 1997) and Ellipsoidal ARTMAP (2001)). In this dissertation, we focus on the evolutionary optimization of ART neural network architectures with the intent of optimizing the size and the generalization performance of the ART neural network. A number of researchers have focused on the evolutionary optimization of neural networks, but no research has …
Genetically Engineered Adaptive Resonance Theory (Art) Neural Network Architectures, Ahmad Al-Daraiseh
Genetically Engineered Adaptive Resonance Theory (Art) Neural Network Architectures, Ahmad Al-Daraiseh
Electronic Theses and Dissertations
Fuzzy ARTMAP (FAM) is currently considered to be one of the premier neural network architectures in solving classification problems. One of the limitations of Fuzzy ARTMAP that has been extensively reported in the literature is the category proliferation problem. That is Fuzzy ARTMAP has the tendency of increasing its network size, as it is confronted with more and more data, especially if the data is of noisy and/or overlapping nature. To remedy this problem a number of researchers have designed modifications to the training phase of Fuzzy ARTMAP that had the beneficial effect of reducing this phenomenon. In this thesis …