Design of FIR Digital Filters Using Hopfield Neural Network

  • Authors:

  • Yue-Dar Jou;Fu-Kun Chen

  • Affiliations:

  • The author was with the Department of Computer and Information Science and now is with the Department of Electrical Engineering, R.O.C. Military Academy, Taiwan. E-mail: ydjou@cc.cma.edu.tw,;The author is with the Department of Computer Science and Information Engineering, Southern Taiwan University, Taiwan. E-mail: fkchen@ieee.org

  • Venue:
  • IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
  • Year:
  • 2007

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Abstract

This paper is intended to provide an alternative approach for the design of FIR filters by using a Hopfield Neural Network (HNN). The proposed approach establishes the error function between the amplitude response of the desired FIR filter and the designed one as a Lyapunov energy function to find the HNN parameters. Using the framework of HNN, the optimal filter coefficients can be obtained from the output state of the network. With the advantages of local connectivity, regularity and modularity, the architecture of the proposed approach can be applied to the design of differentiators and Hilbert transformer with significantly reduction of computational complexity and hardware cost. As the simulation results illustrate, the proposed neural-based method is capable of achieving an excellent performance for filter design.