Fully-Programmable Analogue VLSI Devices for the Implementation of Neural Networks

1989 ◽  
pp. 236-244 ◽  
Author(s):  
Alan Murray ◽  
Anthony Smith ◽  
Lionel Tarassenko
Keyword(s):  
Neural Networks ◽  
Analogue Vlsi

ON-CHIP LEARNING WITH ANALOGUE VLSI NEURAL NETWORKS

1993 ◽  
Vol 04 (04) ◽  
pp. 419-426 ◽  
Author(s):  
LIONEL TARASSENKO ◽  
JON TOMBS ◽  
GRAHAM CAIRNS
Keyword(s):  
Neural Networks ◽  
Synaptic Weight ◽  
Robot Navigation ◽  
Classification Problem ◽  
Classification Performance ◽  
Mobile Robot Navigation ◽  
Navigation Data ◽  
Analogue Vlsi ◽  
On Chip ◽  
Measurable Change

Results from simulations of weight perturbation as an on-chip learning scheme for analogue VLSI neural networks are presented. The limitations of analogue hardware are modelled as realistically as possible. Thus synaptic weight precision is defined according to the smallest change in the weight setting voltage which gives a measurable change at the output of the corresponding neuron. Tests are carried out on a hard classification problem constructed from mobile robot navigation data. The simulations show that the degradation in classification performance on a 500-pattern test set caused by the introduction of realistic hardware constraints is acceptable: with 8-bit weights, updated probabilistically and with a simplified output error criterion, the error rate increases by no more than 7% when compared with weight perturbation implemented with full 32-bit precision.

A programmable CMOS gaussian synapse for analogue VLSI neural networks

1997 ◽  
Vol 83 (1) ◽  
pp. 91-98 ◽  
Author(s):  
K. T. LAU ◽  
S. T. LEE
Keyword(s):  
Neural Networks ◽  
Analogue Vlsi

Determination of S-N curves with the application of artificial neural networks

1999 ◽  
Vol 22 (8) ◽  
pp. 723-728 ◽  
Author(s):  
Artymiak ◽  
Bukowski ◽  
Feliks ◽  
Narberhaus ◽  
Zenner
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural

Review of Pattern Recognition by Self-Organizing Neural Networks.

1995 ◽  
Vol 40 (11) ◽  
pp. 1110-1110
Author(s):  
Stephen James Thomas
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Self Organizing
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