STRIP - a strip-based neural-network growth algorithm for learning multiple-valued functions

We consider the problem of synthesizing multiple-valued logic functions by neural networks. A genetic algorithm (GA) which finds the longest strip in V/spl sube/K/sup n/ is described. A strip contains points located between two parallel hyperplanes. Repeated application of GA partitions the space V...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2001-03, Vol.12 (2), p.212-227
Main Authors: Ngom, A., Stojmenovic, I., Milutinovic, V.
Format: Article
Language:English
Subjects:
Algebra
Algorithms
Computer science
Genetic algorithms
Hyperplanes
Learning
Logic functions
Mathematical analysis
Mathematical models
Multi-layer neural network
Network synthesis
Neural networks
Neurons
Strip
Strips
Transfer functions
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Summary:We consider the problem of synthesizing multiple-valued logic functions by neural networks. A genetic algorithm (GA) which finds the longest strip in V/spl sube/K/sup n/ is described. A strip contains points located between two parallel hyperplanes. Repeated application of GA partitions the space V into certain number of strips, each of them corresponding to a hidden unit. We construct two neural networks based on these hidden units and show that they correctly compute the given but arbitrary multiple-valued function. Preliminary experimental results are presented and discussed.
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/72.914519