Neural-network-based fuzzy logic decision systems

Arun D. Kulkarni; G. B. Giridhar; Praveen Coca

doi:10.1117/12.188914

10 October 1994 Neural-network-based fuzzy logic decision systems

Arun D. Kulkarni, G. B. Giridhar, Praveen Coca

Proceedings Volume 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision; (1994) https://doi.org/10.1117/12.188914
Event: Photonics for Industrial Applications, 1994, Boston, MA, United States

Abstract

During the last few years there has been a large and energetic upswing in research efforts aimed at synthesizing fuzzy logic with neural networks. This combination of neural networks and fuzzy logic seems natural because the two approaches generally attack the design of `intelligent' system from quite different angles. Neural networks provide algorithms for learning, classification, and optimization whereas fuzzy logic often deals with issues such as reasoning in a high (semantic or linguistic) level. Consequently the two technologies complement each other. In this paper, we combine neural networks with fuzzy logic techniques. We propose an artificial neural network (ANN) model for a fuzzy logic decision system. The model consists of six layers. The first three layers map the input variables to fuzzy set membership functions. The last three layers implement the decision rules. The model learns the decision rules using a supervised gradient descent procedure. As an illustration we considered two examples. The first example deals with pixel classification in multispectral satellite images. In our second example we used the fuzzy decision system to analyze data from magnetic resonance imaging (MRI) scans for tissue classification.

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Arun D. Kulkarni, G. B. Giridhar, and Praveen Coca "Neural-network-based fuzzy logic decision systems", Proc. SPIE 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision, (10 October 1994); https://doi.org/10.1117/12.188914

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KEYWORDS

Fuzzy logic

Neural networks

Fuzzy systems

Magnetic resonance imaging

Tissues

Data modeling

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