Title :
Complex-valued multistate neural associative memory
Author :
Jankowski, Stanislaw ; Lozowski, Andrzej ; Zurada, Jacek M.
Author_Institution :
Inst. of Electron. Fundamentals, Warsaw Univ. of Technol., Poland
fDate :
11/1/1996 12:00:00 AM
Abstract :
A model of a multivalued associative memory is presented. This memory has the form of a fully connected attractor neural network composed of multistate complex-valued neurons. Such a network is able to perform the task of storing and recalling gray-scale images. It is also shown that the complex-valued fully connected neural network may be considered as a generalization of a Hopfield network containing real-valued neurons. A computational energy function is introduced and evaluated in order to prove network stability for asynchronous dynamics. Storage capacity as related to the number of accessible neuron states is also estimated
Keywords :
Hopfield neural nets; content-addressable storage; Hopfield network; asynchronous dynamics; complex-valued multistate neural associative memory; computational energy function; fully connected attractor neural network; gray-scale image recall; gray-scale image storage; multivalued associative memory; network stability; storage capacity; Associative memory; Computer networks; Gray-scale; Hopfield neural networks; Image coding; Image recognition; Neural networks; Neurons; Stability; State estimation;
Journal_Title :
Neural Networks, IEEE Transactions on