A Full-Differential Analog Design of an Indirect Inverse Control Law Based on Neural Networks
S. Lesueur, Daniel Massicotte, Pierre Sicard · 2006
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