標題: A TRIANGULAR CONNECTION HOPFIELD NEURAL-NETWORK APPROACH TO ANALOG-TO-DIGITAL CONVERSION
作者: CHANG, PR
WANG, BC
GONG, HM
電信工程研究所
Institute of Communications Engineering
公開日期: 1-十二月-1994
摘要: 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.
URI: http://dx.doi.org/10.1109/19.368081
http://hdl.handle.net/11536/2209
ISSN: 0018-9456
DOI: 10.1109/19.368081
期刊: IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Volume: 43
Issue: 6
起始頁: 882
結束頁: 888
顯示於類別:期刊論文


文件中的檔案:

  1. A1994QC35700018.pdf