FPGA implementation of hopfield networks for systems identification

Authors:
Hafida Boumeridja;Miguel Atencia;Gonzalo Joya;Francisco Sandoval
Affiliations:
Departamento de Tecnología Electrónica, Universidad de Málaga, Spain;Departamento de Matemática Aplicada, Universidad de Málaga, Málaga, Spain;Departamento de Tecnología Electrónica, Universidad de Málaga, Spain;Departamento de Tecnología Electrónica, Universidad de Málaga, Spain
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

Citing 4
Cited 1

Robot Dynamics and Control

Robot Dynamics and Control
Dynamically Reconfigurable Hardware - A New Perspective for Neural Network Implementations

FPL '02 Proceedings of the Reconfigurable Computing Is Going Mainstream, 12th International Conference on Field-Programmable Logic and Applications
Parametric identification of robotic systems with stable time-varying Hopfield networks

Neural Computing and Applications
Dynamical Analysis of Continuous Higher-Order Hopfield Networks for Combinatorial Optimization

Neural Computation

FPGA implementation of a systems identification module based upon Hopfield networks

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This contribution presents the hardware implementation of a neural system, which is a variant of a Hopfield network, modified to perform parametric identification of dynamical systems, so that the resulting network possess time-varying weights. The implementation, which is accomplished on FPGA circuits, is carefully designed so that it is able to deal with these dynamic weights, as well as preserve the natural parallelism of neural networks, at a limited cost in terms of occupied area and processing time. The design achieves modularity and flexibility, due to the usage of parametric VHDL to describe the network. The functional simulation and the synthesis show the viability of the design, whose refinement will lead to the development of an embedded adaptive controller for autonomous systems.