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

fYear

1996

fDate

19-22 Jun 1996

Firstpage

336

Lastpage

340

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/NAFIPS.1996.534755

Filename

534755