This paper is concentrated on the Kanerva’s Sparse Distributed Memory as a kind of artificial rieural net and associative memory. SDM eaptures some basic properties of human long-term memory. SDM may be regarded as a three-layered feed-forward neural net. Input layer neurons copy input vectors only, hidden layer neurons have radial basis functions and output layer neurons have linear basis functions. The hidden layer is initialized randomly in the basic SDM algorithm. The aim of the paper is to study of Kanerva’s model behaviour for reál input data (largge input vectors, correlated data). A modification of the basic model is introduced and tested.
This paper is concentrated on the Kanerva’s Sparse Distributed Memory as a kind of artificial rieural iiet and associative memory. SDM captures some basic properties of hnman long-term mernory. SDM may be regarded as a three-layered feed-forward neural net. Input layer neurons copy input vectors only, hidden layer nenrons have radial basis functions and output layer neurons have linear basis functions. The hidden layer is initialized randomly in the basic SDM algorithm. The aim of the paper is to study of Kanerva’s model behaviour for reál input data (largge input vectors, correlated data). A modification of the basic model is introduced and tested.