Page 1. Fuzzy Rule Extraction and Optimization for Rat Sleep- Stage Classification By Raul Cruz-C... more Page 1. Fuzzy Rule Extraction and Optimization for Rat Sleep- Stage Classification By Raul Cruz-Cano, Elvia Martin Del Campo, Patricia A. Nava and Rafael Cabeza University of Texas at El Paso June 2005 Page 2. Outline Introduction Data Acquisition Rule Extraction Method ...
Proceedings of the 5th Biannual World Automation Congress, 2002
This paper presents a description of an algorithm that is used for tuning fuzzy certainty factors... more This paper presents a description of an algorithm that is used for tuning fuzzy certainty factors in a fuzzy expert system. Many expert system building tools have been developed, but only a few can produce systems that deal with inexact knowledge. One of the models that can deal with inexact knowledge is the fuzzy certainty factor expert system. Given a
Computer-produced typeface. Thesis (Ph. D.)--New Mexico State University, 1995. Includes vita. In... more Computer-produced typeface. Thesis (Ph. D.)--New Mexico State University, 1995. Includes vita. Includes bibliographical references (leaves 101-105).
Conference of the North American Fuzzy Information Processing Society, 2001
Neural networks can be used to classify input data into one of a given set of categories. With li... more Neural networks can be used to classify input data into one of a given set of categories. With limited training sets, crisp neural network results are predictably poor. Incorporation of fuzzy techniques improves performance in these cases. Even though fuzzy neural networks classify imprecise data quite well, the incorporation of a soft decision classification lowers the error rate substantially. This
IEEE International Conference on Systems, Man, and Cybernetics, 2001
The performance of neural networks, when the training data is limited, can be improved by incorpo... more The performance of neural networks, when the training data is limited, can be improved by incorporation of interval techniques. These techniques improve performance by introducing the ability to classify imprecise data. Performance can be further improved by incorporating the ability to make soft decisions. Soft decisions differ from hard decisions by allowing the decision-making system the option of deferring to
IEEE International Symposium on Intelligent Control, 1998
Neural network performance is dependent on the quality and quantity of training samples presented... more Neural network performance is dependent on the quality and quantity of training samples presented to the network. In cases where training data is sparse or not fully representative of the range of values possible, incorporation of fuzzy techniques optimizes performance. That is, while neural networks are excellent classifiers, introducing fuzzy techniques allows the classification of imprecise data. The neuro-fuzzy system
Page 1. Fuzzy Rule Extraction and Optimization for Rat Sleep- Stage Classification By Raul Cruz-C... more Page 1. Fuzzy Rule Extraction and Optimization for Rat Sleep- Stage Classification By Raul Cruz-Cano, Elvia Martin Del Campo, Patricia A. Nava and Rafael Cabeza University of Texas at El Paso June 2005 Page 2. Outline Introduction Data Acquisition Rule Extraction Method ...
Proceedings of the 5th Biannual World Automation Congress, 2002
This paper presents a description of an algorithm that is used for tuning fuzzy certainty factors... more This paper presents a description of an algorithm that is used for tuning fuzzy certainty factors in a fuzzy expert system. Many expert system building tools have been developed, but only a few can produce systems that deal with inexact knowledge. One of the models that can deal with inexact knowledge is the fuzzy certainty factor expert system. Given a
Computer-produced typeface. Thesis (Ph. D.)--New Mexico State University, 1995. Includes vita. In... more Computer-produced typeface. Thesis (Ph. D.)--New Mexico State University, 1995. Includes vita. Includes bibliographical references (leaves 101-105).
Conference of the North American Fuzzy Information Processing Society, 2001
Neural networks can be used to classify input data into one of a given set of categories. With li... more Neural networks can be used to classify input data into one of a given set of categories. With limited training sets, crisp neural network results are predictably poor. Incorporation of fuzzy techniques improves performance in these cases. Even though fuzzy neural networks classify imprecise data quite well, the incorporation of a soft decision classification lowers the error rate substantially. This
IEEE International Conference on Systems, Man, and Cybernetics, 2001
The performance of neural networks, when the training data is limited, can be improved by incorpo... more The performance of neural networks, when the training data is limited, can be improved by incorporation of interval techniques. These techniques improve performance by introducing the ability to classify imprecise data. Performance can be further improved by incorporating the ability to make soft decisions. Soft decisions differ from hard decisions by allowing the decision-making system the option of deferring to
IEEE International Symposium on Intelligent Control, 1998
Neural network performance is dependent on the quality and quantity of training samples presented... more Neural network performance is dependent on the quality and quantity of training samples presented to the network. In cases where training data is sparse or not fully representative of the range of values possible, incorporation of fuzzy techniques optimizes performance. That is, while neural networks are excellent classifiers, introducing fuzzy techniques allows the classification of imprecise data. The neuro-fuzzy system
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Papers by Patricia Nava