STAND-ALONE HARDWARE-BASED LEARNING-SYSTEM

被引：0

作者：

CLARKSON, T ^{[1
]}

NG, CK ^{[1
]}

机构：

[1] CITY POLYTECH HONG KONG,DEPT ELECTR ENGN,HONG KONG,HONG KONG

来源：

JAPANESE JOURNAL OF APPLIED PHYSICS PART 1-REGULAR PAPERS SHORT NOTES & REVIEW PAPERS | 1995年 / 34卷 / 2B期

关键词：

NEURAL NETWORKS; VLSI; REINFORCEMENT TRAINING; PRAM;

D O I：

10.1143/JJAP.34.1050

中图分类号：

O59 [应用物理学];

学科分类号：

摘要：

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.

引用

页码：1050 / 1055

页数：6

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