Low Cost Neural Network Hardware for Control School of Computing Science and Software Engineering, Queensland University of Technology

Format:

Conference/Event

Author/Creator:

Sitte, Joaquin, author.

Conference Name:

Automotive and Transportation Technology Congress and Exposition (2001-10-01 : Barcelona, Spain)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2001

Summary:

Feedforward artificial neural networks are universal function approximators and inherently parallel computing structures. Because of the lack of appropriate hardware realisations, applications of neural networks are predominantly implemented as sequential programs on digital processors. In this paper we describe an analogue integrated circuit realisation of a local response neural network (LCNN) that achieves a high degree of parallel computation in a small size, low cost and low power consumption. Because it can directly receive analog inputs from sensors and output analog control signals to actuators it is well suited as a building block for real-time control systems

Notes:

Vendor supplied data

Publisher Number:

2001-01-3397

Access Restriction:

Restricted for use by site license