A full-differential analog design of an indirect inverse control law based on neural networks

Author

Lesueur, Sébastien ; Massicotte, Daniel ; Sicard, Pierre

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. du Quebec a Trois-Rivieres, Que.

fYear

2006

fDate

21-24 May 2006

Lastpage

2784

Abstract

This paper presents a full-differential analog design of an indirect inverse control law based on dynamic back propagation neural networks developed. The on-line adaptation algorithms of the synaptic weights are modeled by means of continuous-time integration circuits. The simulation results obtained at a post-layout simulation level show a very good computing precision as well as interesting power consumption and integration area. The speed of the circuit is largely sufficient to meet real-time requirements in numerous applications of the control fields

Keywords

VLSI; analogue integrated circuits; backpropagation; neural nets; back propagation neural networks; continuous-time integration circuits; full-differential analog design; indirect inverse control law; online adaptation algorithms; post-layout simulation; synaptic weights; Circuit noise; Circuit simulation; Computational modeling; Computer networks; Control systems; Feedforward neural networks; Neural networks; Process control; Signal processing algorithms; Very large scale integration;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2006. ISCAS 2006. Proceedings. 2006 IEEE International Symposium on

Conference_Location

Island of Kos

Print_ISBN

0-7803-9389-9

Type

conf

DOI

10.1109/ISCAS.2006.1693201

Filename

1693201