For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Digital Neuron Model Using Digital Phase-Locked Loop
Manabu TOKUNAGA Iwo SASASE Shinsaku MORI
IEICE TRANSACTIONS on Information and Systems
Publication Date: 1991/03/25
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Artificial Intelligence and Cognitive Science
Full Text: PDF>>
We propose a new type of the digital neuron model by using multi-input multilevel-quanitized digital phase-locked loop (MM-DPLL), where the input is represented by the phase modulated signal. It is shown that this model has the characteristics of the neuron; spatial summation, temporal summation and thresholding. We applied our model to the pattern recognition and to the Hopfield type associative memory, in order to verify that the network by this model can operate properly. In the pattern recognition, we used the perceptron convergence procedure (delta rule), and confirm the possibility of learning by modifying the connection weights. In the associative memory, we confirm that the network can learn five digit patterns of the fundamental memories, and also can recall the correct pattern for the noisy input pattern.