Stand-alone hardware-based learning system

The probabilistic Random Access Memory (pRAM) is a biologically-inspired model of a neuron. The pRAM behaviour is described in this paper in relation to binary and real-valued input vectors. The pRAM is hardware-realisable, as is its reinforcement training algorithm. The pRAM model may be applied to...

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Bibliographic Details
Published in:Japanese Journal of Applied Physics 1995-02, Vol.34 (2B), p.1050-1055
Main Authors: CLARKSON, T, CHI KWONG NG
Format: Article
Language:English
Subjects:
Applied sciences
Electric, optical and optoelectronic circuits
Electronics
Exact sciences and technology
Neural networks
Online Access:Get full text
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Description
Summary:The probabilistic Random Access Memory (pRAM) is a biologically-inspired model of a neuron. The pRAM behaviour is described in this paper in relation to binary and real-valued input vectors. The pRAM is hardware-realisable, as is its reinforcement training algorithm. The pRAM model may be applied to a wide range of artificial neural network applications, many of which are classification tasks. The application presented here is a control problem where an inverted pendulum, mounted on a cart, is to be balanced. The solution to this problem using the pRAM-256, a VLSI pRAM controller, is shown.
ISSN:0021-4922
1347-4065
DOI:10.1143/JJAP.34.1050