**
#54:
Y. Luo,
K. Palaniappan, and
Y. Li
**
*Lecture Notes in Computer Science (Advances in Natural Computation),
Volume 3612,
pgs. 1132--1141,
2005*

Abstract,

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PlainText,

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DOI,

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Min-implication fuzzy relation equations based on Boolean- type implications can also be viewed as a way of implementing fuzzy associative memories with perfect recall. In this paper, fuzzy associative memories with perfect recall are constructed, and new on-line learning algorithms adapting the weights of its interconnections are incorporated into this neural network when the solution set of the fuzzy relation equa- tion is non-empty. These weight matrices are actually the least solution matrix and all maximal solution matrices of the fuzzy relation equation, respectively. The complete solution set of min-implication fuzzy relation equation can be determined by the maximal solution set of this equation.

@article{Palani:Fuzzy-2005,
author = "Y. Luo and K. Palaniappan and Y. Li",
title = "New algorithms of neural fuzzy relation systems with min-implication composition",
year = 2005,
journal = "Lecture Notes in Computer Science (Advances in Natural Computation)",
volume = 3612,
pages = "1132--1141",
keywords = "data mining, machine learning, classification",
doi = "10.1007/11539902_143"
}

Y. Luo, K. Palaniappan, and Y. Li. New algorithms of neural fuzzy relation systems with min-implication composition. Lecture Notes in Computer Science (Advances in Natural Computation), volume 3612, pages 1132--1141, 2005.