Abstract
Fuzzy Associative Memory (FAM) is a neural network that stores associations of patterns. The most important advantage of FAM is recalling stored patterns from noisy inputs (noise tolerance). Some FAMs only show associations or content of pattern separately. Therefore, we propose a model of FAM that shows both associations and content of patterns effectively. In learning process, each pair of pattern is learned by the minimum of input and output pattern. Then, all pairs of pattern are generalized by mean of the learning results of each pair. In recalling process, a new threshold is added to improve noise tolerance. We have conducted experiments in pattern recognition to prove the effectiveness of our FAM. Experiment results show that our model tolerates noise better than previous FAMs in two types of noise.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-05939-6_37
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© 2014 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Binh, P.V., Hoa, N.T., Thai, V.D., Truong, Q.X. (2014). A New Fuzzy Associative Memory. In: Vinh, P., Alagar, V., Vassev, E., Khare, A. (eds) Context-Aware Systems and Applications. ICCASA 2013. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-319-05939-6_22
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DOI: https://doi.org/10.1007/978-3-319-05939-6_22
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