Title :
A neuro-fuzzy computing model of human pattern generation
Author :
Hata, Yutaka ; Lee, Michael A. ; Yamato, Kazuharu
Author_Institution :
Div. of Comput. Sci., California Univ., Berkeley, CA, USA
Abstract :
Investigates a novel technique for constructing and evaluating neuro-fuzzy models of human pattern generation. The modeling approach consists of two steps: first, a neural network is trained to learn a core concept, and then the trained network is augmented with additional processing nodes and connections. The augmented network is then tested on its ability to solve problems related to the core concept for which it was trained. We present results from applying our model to image generation and decision making
Keywords :
brain models; cognitive systems; fuzzy neural nets; learning (artificial intelligence); pattern recognition; problem solving; additional connections; additional processing nodes; augmented network; core concept learning; decision making; human pattern generation; image generation; neural network training; neuro-fuzzy computing model; problem-solving ability; Adaptive systems; Decision making; Feedforward neural networks; Feedforward systems; Humans; Image generation; Logic; NASA; Neural networks; Testing;
Conference_Titel :
Fuzzy Information Processing Society, 1996. NAFIPS., 1996 Biennial Conference of the North American
Conference_Location :
Berkeley, CA
Print_ISBN :
0-7803-3225-3
DOI :
10.1109/NAFIPS.1996.534755
