A TRIANGULAR CONNECTION HOPFIELD NEURAL-NETWORK APPROACH TO ANALOG-TO-DIGITAL CONVERSION

Abstract

A Hopfield-type neural network approach which leads to an analog circuit for implementing the A/D conversion is presented. The solution of the original symmetric connection Hopfield A/D converter sometimes may reach a ''spurious state'' that does not correspond to the correct digital representation of the input signal. An A/D converter based on the model of nonsymmetrical neural networks is proposed to obtain the stable and correct encoding. Due to the infeasible conventional RC-active implementation, a cost-effective switched-capacitor implementation by means of Schmitt triggers is adopted. It is capable of achieving high performance as well as a high convergence rate. Finally, a simulation using a tool called SWITCAP is conducted to verify the validity and performance of the proposed implementation.