#53: New algorithms of neural fuzzy relation systems with min-implication composition


Y. Luo, K. Palaniappan, and Y. Li

Lecture Notes in Computer Science (Advances in Natural Computation), Volume 3612, pgs. 1132--1141, 2005

data mining, machine learning, classification

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Abstract

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.