A New Model of a Fuzzy Associative Memory

2015 ◽  
pp. 32-42
Author(s):  
Irina Perfilieva
Keyword(s):  
Associative Memory ◽  
New Model ◽  
Fuzzy Associative Memory

An Approach to Artificial Concept Learning Based on Human Concept Learning by Using Artificial Neural Networks

2011 ◽  
pp. 130-145
Author(s):  
Enrique Mérida-Casermeiro ◽  
Domingo López-Rodríguez ◽  
J.M. Ortiz-de-Lazcano-Lobato
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Associative Memory ◽  
Concept Learning ◽  
Energy Function ◽  
Hebbian Learning ◽  
Clustering Algorithms ◽  
Network Capacity ◽  
New Model

In this chapter, two important issues concerning associative memory by neural networks are studied: a new model of hebbian learning, as well as the effect of the network capacity when retrieving patterns and performing clustering tasks. Particularly, an explanation of the energy function when the capacity is exceeded: the limitation in pattern storage implies that similar patterns are going to be identified by the network, therefore forming different clusters. This ability can be translated as an unsupervised learning of pattern clusters, with one major advantage over most clustering algorithms: the number of data classes is automatically learned, as confirmed by the experiments. Two methods to reinforce learning are proposed to improve the quality of the clustering, by enhancing the learning of patterns relationships. As a related issue, a study on the net capacity, depending on the number of neurons and possible outputs, is presented, and some interesting conclusions are commented.

FUZZY ASSOCIATIVE MEMORY APPLICATIONS TO CONTROL

1991 ◽  
pp. 1249-1252 ◽  
Author(s):  
T. Yamaguchi ◽  
M. Tanabe ◽  
T. Takagi
Keyword(s):  
Associative Memory ◽  
Fuzzy Associative Memory ◽  
Memory Applications

Sparse Associative Memory

2019 ◽  
Vol 31 (5) ◽  
pp. 998-1014 ◽  
Author(s):  
Heiko Hoffmann
Keyword(s):  
Associative Memory ◽  
Building Block ◽  
Basic Building Block ◽  
Small Probability ◽  
Hopfield Networks ◽  
New Model ◽  
Brain Functions ◽  
Memory Efficiency ◽  
Low Efficiency

It is still unknown how associative biological memories operate. Hopfield networks are popular models of associative memory, but they suffer from spurious memories and low efficiency. Here, we present a new model of an associative memory that overcomes these deficiencies. We call this model sparse associative memory (SAM) because it is based on sparse projections from neural patterns to pattern-specific neurons. These sparse projections have been shown to be sufficient to uniquely encode a neural pattern. Based on this principle, we investigate theoretically and in simulation our SAM model, which turns out to have high memory efficiency and a vanishingly small probability of spurious memories. This model may serve as a basic building block of brain functions involving associative memory.

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