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A triangular connection Hopfield neural network approach to analog-to-digital conversion

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3 Author(s)
Po-Rong Chang ; Dept. of Commun. Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan ; Bor-Chin Wang ; H. M. Gong

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

Published in:

IEEE Transactions on Instrumentation and Measurement  (Volume:43 ,  Issue: 6 )