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Real-time neural networks: conjunctoid parallel implementation

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2 Author(s)
Mehta, P. ; Dept. of Electr. & Comput. Eng., South Carolina Univ., Columbia, SC, USA ; Jannarone, R.

Conjunctoids are model-based neural networks for categorical data, having features that include: generality, with special cases ranging from simple perceptron-like linear versions to full-blown versions that account for all possible associations among external variables; continuous learning and performance, with provisions for optimal updating as each new datum is received, based on Bayes decision theory; and separable learning as well as performance formulas, with provisions for breaking down necessary global computations into parallel components. In the paper, a simple PC implementation is described for a full-blown conjunctoid model on a small-scale setting. A design and implementation of the model on an NCUBE parallel platform and on a special purpose parallel platform are also described

Published in:

System Theory, 1991. Proceedings., Twenty-Third Southeastern Symposium on

Date of Conference:

10-12 Mar 1991

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